Appearance-driven automatic three-dimensional modeling

ABSTRACT

Appearance driven automatic three-dimensional (3D) modeling enables optimization of a 3D model comprising the shape and appearance of a particular 3D scene or object. Triangle meshes and shading models may be jointly optimized to match the appearance of a reference 3D model based on reference images of the reference 3D model. Compared with the reference 3D model, the optimized 3D model is a lower resolution 3D model that can be rendered in less time. More specifically, the optimized 3D model may include fewer geometric primitives compared with the reference 3D model. In contrast with the conventional inverse rendering or analysis-by-synthesis modeling tools, the shape and appearance representations of the 3D model are automatically generated that, when rendered, match the reference images. Appearance driven automatic 3D modeling has a number of uses, including appearance-preserving simplification of extremely complex assets, conversion between rendering systems, and even conversion between geometric scene representations.

CLAIM OF PRIORITY

This application is a continuation of U.S. Non-Provisional applicationSer. No. 17/194,477 titled “Appearance-Driven AutomaticThree-Dimensional Modeling,” filed Mar. 8, 2021 which claims the benefitof U.S. Provisional Application No. 63/116,294 titled “Appearance-DrivenAutomatic Three-Dimensional Modeling,” filed Nov. 20, 2020, the entirecontents of both are incorporated herein by reference.

BACKGROUND

Synthesizing images of objects with complex shapes and appearances is acentral goal in computer graphics. The problem can be broken down intochoosing suitable representations for shape and appearance of theobjects, modeling a three-dimensional (3D) scene according to the chosenrepresentations, and finally, rendering the scene efficiently. Creatinga shape and appearance model for a particular 3D scene is inherently aninverse problem: seeking representation that will, when rendered, resultin a two-dimensional (2D) image that appears as desired. Over multipleiterations, inverse rendering is used to iteratively recover a shape,lighting, and material properties of a 3D model based on 2D images.Inverse rendering is challenging because the operations used to renderthe 3D model to produce the 2D images cannot simply be performed inreverse to generate the 3D model from the 2D images. Therefore,conventional modeling tools turn the problem around: instead ofproviding the user with means to specify the image they want,conventional modeling tools provide tools for editing the scenerepresentation, leaving the modeler to manually modify the scenerepresentation iteratively to achieve their goal. There is a need foraddressing these issues and/or other issues associated with the priorart.

SUMMARY

Embodiments of the present disclosure relate to appearance drivenautomatic three-dimensional (3D) modeling. Systems and methods aredisclosed that enable optimization of a 3D model comprising the shapeand appearance of a particular 3D scene or object. In an embodiment,triangle meshes and shading models are jointly optimized to match theappearance of reference 3D models based on reference images of thereference 3D models. Compared with the reference 3D model, the optimized3D model is a lower resolution 3D model that can be rendered in lesstime. More specifically, the optimized 3D model may include fewergeometric primitives compared with the reference 3D model. Appearancedriven automatic 3D modeling has a number of uses, includingappearance-preserving simplification of extremely complex assets,conversion between different rendering systems, and even conversionbetween different geometric scene representations.

In contrast with the conventional inverse rendering oranalysis-by-synthesis modeling tools, the shape and appearancerepresentations of the 3D model are automatically generated that, whenrendered, match the reference images. In other words, the modeler neednot manually modify the scene representation iteratively to optimize a3D model. In an embodiment, shape optimization of the 3D model is basedon deforming an existing triangular mesh, relying on multiple referenceimages to allow higher quality reconstruction that includes materials inthe representation. In contrast with approaches that train a neuralnetwork to convert a higher resolution 3D model into a lower resolution3D model, the representation is directly generated based on image spacedifferences.

A method, computer readable medium, and system are disclosed forappearance-driven automatic 3D modeling. An initial 3D model isprocessed to produce a set of images for environmental conditionsspecifying at least one of camera position or light position. Areference 3D model is rendered to produce a set of reference images forthe environmental conditions and image space losses are computed basedon the set of images and the set of reference images. Parameters of theinitial 3D model are updated according to the image space losses toproduce a reduced resolution 3D model having a lower resolution comparedwith a resolution of the reference 3D model.

BRIEF DESCRIPTION OF THE DRAWINGS

The present systems and methods for appearance driven automatic 3Dmodeling are described in detail below with reference to the attacheddrawing figures, wherein:

FIG. 1A illustrates a conceptual diagram of an appearance drivenautomatic 3D modeling system, in accordance with an embodiment.

FIG. 1B illustrates a flowchart of a method for appearance drivenautomatic 3D modeling suitable for use in implementing some embodimentsof the present disclosure.

FIG. 1C illustrates a diagram of another appearance driven automatic 3Dmodeling system suitable for use in implementing some embodiments of thepresent disclosure.

FIG. 2A illustrates a conceptual diagram of normal vector anddisplacement mapping for a 3D model, in accordance with an embodiment.

FIG. 2B illustrates a conceptual diagram of normal vector and a newlighting scenario for a 3D model, in accordance with an embodiment.

FIG. 2C illustrates a conceptual diagram of appearance pre-filtering fora 3D model, in accordance with an embodiment.

FIG. 2D illustrates a conceptual diagram of simplification of aggregategeometry for a 3D model, in accordance with an embodiment.

FIG. 3 illustrates a block diagram of the rendering pipeline shown inFIGS. 1A and 1C suitable for use in implementing some embodiments of thepresent disclosure.

FIG. 4 illustrates an example parallel processing unit suitable for usein implementing some embodiments of the present disclosure.

FIG. 5A is a conceptual diagram of a processing system implemented usingthe PPU of FIG. 4 , suitable for use in implementing some embodiments ofthe present disclosure.

FIG. 5B illustrates an exemplary system in which the variousarchitecture and/or functionality of the various previous embodimentsmay be implemented.

FIG. 5C illustrates components of an exemplary system that can be usedto train and utilize machine learning, in at least one embodiment.

FIG. 6A is a conceptual diagram of a graphics processing pipelineimplemented by the PPU of FIG. 4 suitable for use in implementing someembodiments of the present disclosure.

FIG. 6B illustrates an exemplary game streaming system suitable for usein implementing some embodiments of the present disclosure.

DETAILED DESCRIPTION

Systems and methods are disclosed related to appearance driven automatic3D modeling. The optimized (simplified) 3D model is produced startingwith an initial 3D model (e.g., sphere or reduced resolution version ofthe reference 3D model) and parameters of the initial 3D model areupdated to produce the optimized 3D model. The initial 3D model is aninitial guess for beginning an optimization process to produce theoptimized or reduced resolution 3D model. The parameters are updatedbased on image space losses (e.g., pixel differences) between renderedimages of the initial 3D model and the reference 3D model.

A differentiable renderer and non-linear optimization algorithms areused to develop the optimized 3D model by minimizing image spacedifferences that are computed based on images of the 3D model and imagesof a reference 3D model rendered in similar viewing and lightingconditions. Success is measured according to whether rendered images ofthe 3D model are visually identical or nearly identical compared withthe rendered images of the reference 3D model. As the only signalsdriving the optimization are differences in the rendered images, theapproach is highly general and versatile: it easily supports manydifferent forward rendering models such as normal mapping,spatially-varying bi-directional reflectance distribution functions(BRDFs), displacement mapping, and the like. Supervision through imagesonly is also key to the ability to easily convert between renderingsystems and scene representations.

In an embodiment, the generated 3D models are represented as trianglemeshes with materials encoded as textures to ensure that the 3D modelsrender efficiently on modern graphics hardware and benefit from, e.g.,hardware-accelerated rasterization, ray-tracing, and filtered texturelookups. The automatically generated 3D models may be used for meshdecimation through joint shape and-appearance simplification, optimizedlevel-of-detail generation for reduced aliasing, seamless meshfiltering, approximations of aggregate geometry, joint optimization ofshape and skinning weights to produce reduced geometry for animation,conversion between shape representations, and conversion betweenrendering systems.

Because the appearance driven modeling technique is based on inverserendering and non-linear optimization, it easily generalizes overdifferent regimes while allowing joint optimization of all aspects ofthe representation that affect the final appearance of the optimized 3Dmodel. Specifically, in an embodiment, the search for the shape andappearance of the 3D model is driven by image space error and the shapeand appearance are simultaneously optimized. Therefore, each stage ofthe forward rendering model (e.g., geometry, shading, normal maps,displacement maps, transparency, and the like) can specialize for theeffects that the particular stage captures best, “negotiating” how toachieve the desired outcome together. As an example, a natural divisionof labor between the geometry (mesh) and a normal map occurs: geometricdetail is allowed to move between the representations by, e.g., locallysmoothing a mesh and baking geometric detail into the normal map orother parameters of a physically-based shading model.

FIG. 1A illustrates a conceptual diagram of an appearance drivenautomatic 3D modeling system 150, in accordance with an embodiment. Aninitial 3D model 105 representation may include geometry defined bylocations of vertices in 3D space that form a mesh (e.g., sphere orother geometric shape) and parameters 106. In an embodiment, the mesh isdefined by other types of primitives or representations. The initial 3Dmodel 105 is adjusted to produce a reduced resolution 3D model 110. Thereduced resolution 3D model 110 is reduced in terms of complexity and/orgeometric resolution (e.g., number of primitives and/or vertices)compared with a reference 3D model. In an embodiment, the number ofprimitives defining the reduced resolution 3D model 110 is equal orgreater than the number of primitives defining the initial 3D model 105.

The parameters 106 of the initial 3D model 105 and parameters 116 of thereduced resolution (optimized) 3D model 110 may correspond to materialsdefined by spatially-varying BRDF. The parameters 106 and 116 mayinclude normal vector maps, displacement maps, texture maps, skinningweights, and the like. More specifically, the texture maps may definelighting and material properties. An initial surface texture for theinitial 3D model 105 may be a constant or randomized color and thecolors of each texel in the initial surface texture are adjusted basedon image space differences to produce a texture for the reducedresolution 3D model 110.

Environmental conditions 108 define camera and light positions used by arendering pipeline 100 for producing each image of the images 112. Therendering pipeline 100 renders the initial 3D model 105 according to theenvironmental conditions 108 to produce one or more of the images 112.In an embodiment, the initial 3D model 105 is a base model that isdeformed to produce a specific 3D model corresponding to each of the 2Dimages in the images 112. Reference images 124 are generated byrendering a reference 3D model according to the environmental conditions108. In an embodiment, the reference images 124 comprise a video oranimation sequence of the reference 3D model in motion and/or deforming.As shown in FIG. 1A, the images 112 include a rendered 3D modelrepresentation 115 and the reference images 124 include a renderedreference 3D model 125. While the initial 3D model 105 may not closelyresemble the reference 3D model, after successful optimization, thereduced resolution 3D model 110 does closely resemble the reference 3Dmodel.

In an embodiment, the rendering pipeline 100 is a differentiablerenderer and, one or more operations of the differentiable renderer areperformed using any combination of a graphics processing unit (GPU)graphics pipeline, GPU general computation cores, or on a centralprocessing unit (CPU). The differentiable renderer enables operationssuch as rasterizing large numbers of triangles, attribute interpolation,filtered texture lookups, as well as user-programmable shading andgeometry processing, all in high resolutions. In contrast withconventional rendering pipelines, the operations performed by therendering pipeline 100 are differentiable and image space losses 122 maybe propagated backwards through the rendering pipeline 100 toiteratively adjust the reduced resolution 3D model 110. In someembodiments, rendering operations may include rasterization, raytracing, and path tracing.

An image space loss unit 120 processes the reference images 124 and theimages 112 to produce the image space losses 122. Corresponding imagesfrom the images 112 and reference images 124 for each particularenvironmental configuration 108 are compared to compute the image spacelosses 122. The initial 3D model 105 is adjusted based on the imagespace losses 122 to produce the reduced resolution 3D model 110. Thereduced resolution 3D model 110 may be further refined until the imagespace losses 122 are reduced to a target value. In an embodiment, thereduced resolution 3D model 110 comprises a mesh having a higherresolution (e.g., more triangles) compared with the initial 3D model105, but the resolution of the reference 3D model is even higherresolution compared with the reduced resolution 3D model 110. Inresponse to the image space losses 122, the reduced resolution 3D model110 may be further tessellated during optimization to reduce the imagespace losses 122. As shown in FIG. 1A, the reduced resolution 3D model110 for the particular example is represented by 3 k triangle primitivesdefined by vertex positions and normal vectors. In contrast, thereference 3D model is represented by 735 k triangle primitives. In anembodiment, a normal vector is associated with each vertex and thenormal vector is perpendicular to a surface of the triangle primitive atthe vertex.

A shaded portion of the skull shown in the reduced resolution 3D model110 illustrates the tangent space normal vectors that are stored as atexture. The normal map for the reduced resolution 3D model 110 can bemuch higher resolution compared with the number of vertices of thereduced resolution 3D model 110, so that for each triangle, manydifferent normal vectors may be read from the texture over the surfaceof the triangle. Using a higher resolution for the parameters is a keyenabler for creating a reduced resolution version of a reference 3Dmodel with many triangles. For example, geometric details of the meshare baked (e.g., encoded) into the normal map texture.

The goal of the appearance driven automatic 3D modeling system 150 is toproduce the reduced resolution 3D model 110 that, when renderedaccording to the environmental conditions 108 produces a set of renderedimages 112 that closely match the reference images 124. Unlikeconventional modeling systems having a goal of accurately reconstructingthe reference 3D model, the appearance driven automatic 3D modelingsystem 150 generates the reduced resolution 3D model 110 that can berendered to produce images that closely match images of the reference 3Dmodel. In other words, the rendered images are processed to determineand fine-tune the geometry that defines the 3D model and there is noneed to directly compare the representation of the reduced resolution 3Dmodel 110 with the reference 3D model. In fact, the representation ofthe reference 3D model may be quite different compared with therepresentation of the reduced resolution 3D model 110.

In contrast to algorithms like multi-view stereo that must make do witha small number of reference images, appearance driven automatic 3Dmodeling is well-suited for applications where it is possible toprogrammatically synthesize reference views of a target scene underarbitrary, controllable viewing and lighting conditions. The objectivefunction used by the image space loss unit 122 is based on visualdifferences and gradient-based optimization may be leveraged throughdifferentiable rendering within the rendering pipeline 100.

More illustrative information will now be set forth regarding variousoptional architectures and features with which the foregoing frameworkmay be implemented, per the desires of the user. It should be stronglynoted that the following information is set forth for illustrativepurposes and should not be construed as limiting in any manner. Any ofthe following features may be optionally incorporated with or withoutthe exclusion of other features described.

FIG. 1B illustrates a flowchart of a method 130 for appearance drivenautomatic 3D modeling suitable for use in implementing some embodimentsof the present disclosure. Each block of method 130, described herein,comprises a computing process that may be performed using anycombination of hardware, firmware, and/or software. For instance,various functions may be carried out by a processor executinginstructions stored in memory. The method may also be embodied ascomputer-usable instructions stored on computer storage media. Themethod may be provided by a standalone application, a service or hostedservice (standalone or in combination with another hosted service), or aplug-in to another product, to name a few. In addition, method 130 isdescribed, by way of example, with respect to the appearance drivenautomatic 3D modeling system 150 of FIG. 1A. However, this method mayadditionally or alternatively be executed by any one system, or anycombination of systems, including, but not limited to, those describedherein. Furthermore, persons of ordinary skill in the art willunderstand that any system that performs method 130 is within the scopeand spirit of embodiments of the present disclosure.

At step 135, an initial 3D model is processed by to produce a set ofimages for environmental conditions specifying at least one of cameraposition or light position. In an embodiment, the initial 3D model isthe initial 3D model 105 that is processed by the rendering pipeline100. In an embodiment, the initial 3D model comprises a sphere. In anembodiment, the initial 3D model is a latent representation that isunobserved and inferred from image space differences. In an embodiment,the latent representation comprises a triangle mesh and a set oftextures that describe spatially varying material from aphysically-based shading model.

In an embodiment, a rendering pipeline produces the set of images. In anembodiment, the rendering pipeline is a differentiable renderer andimage space losses are propagated backwards through the renderingpipeline to update the parameters. In an embodiment, the parameterscomprise at least one of vertex locations, surface normal vectors,materials, a displacement map, skinning weights, or a texture map. In anembodiment, the rendering pipeline comprises a sequence of meshoperations, a rasterizer, and a deferred shading stage. In anembodiment, the rendering pipeline performs an analytic antialiasingoperation that determines shaded pixel values based on geometriccoverage after rasterization, visibility testing, and texture mapping isperformed.

At step 140, a reference 3D model is rendered to produce a set ofreference images for the environmental conditions. In an embodiment, thereference 3D model is rendered by a second rendering pipeline that has adifferent structure compared with the rendering pipeline. In anembodiment, the initial 3D model comprises a decimated version of thereference 3D model. In an embodiment, the reference images areanti-aliased and the initial 3D model is processed by the renderingpipeline using fewer samples per-pixel, compared with the number ofsamples used to render the reference images, to produce the set ofimages. In an embodiment, the set of reference images comprise ananimation sequence and the parameters comprise skinning weights.

At step 145, image space losses are computed based on the set of imagesand the set of reference images. In an embodiment, the image spacelosses are computed by the image space loss unit 120. In an embodiment,the set of images and the set of reference images each include at leastone image. In an embodiment, the image space losses are back propagatedthrough the rendering pipeline to update the initial 3D model andimprove visual similarity between the sets of images and referenceimages. In an embodiment, the image space losses are back propagated asgradients that indicate the effect of moving mesh vertex positions andadjusting other parameters has on the set of images. Operationsperformed by the rendering pipeline to produce the set of images aredifferentiable, so that the gradients may be computed forbackpropagation.

In an embodiment, the image space losses are computed using stochasticgradient descent. In an embodiment, an image from the set of images anda corresponding image from the set of reference images for each one ofthe environmental conditions are compared to compute the image spacelosses. In an embodiment, the environmental conditions comprise randomcamera and light positions. In an embodiment, the light is a singlepoint light source similar to a virtual photo-goniometer.

At step 155, parameters of the initial 3D model are updated according tothe image space losses to produce a reduced resolution 3D model having alower resolution compared with a resolution of the reference 3D model.In an embodiment, corrections for the parameters are generatedsimultaneously to update the parameters. In an embodiment, the reducedresolution 3D model comprises a first geometric representation and thereference 3D model comprises a second geometric representation that isdifferent from the first geometric representation. The reducedresolution 3D model comprises an optimized representation of thereference 3D model that may replace the reference 3D model for games orother real-time applications.

Step 135 may be repeated, but instead of processing the initial 3Dmodel, the reduced resolution 3D model is processed to produce at leastone additional image. Steps 140 and 145 may be repeated, for the atleast one additional image and at least one additional reference image.Finally, step 155 may be repeated, but instead of updating theparameters of the initial 3D model, the parameters of the reducedresolution 3D model are updated to optimize the reduced resolution 3Dmodel over one or more iterations.

FIG. 1C illustrates a diagram of another appearance driven automatic 3Dmodeling system suitable for use in implementing some embodiments of thepresent disclosure. In addition to the rendering pipeline 100 and theimage space loss unit 120 from the appearance driven automatic 3Dmodeling system 150, the system also includes a rendering pipeline 160.In an embodiment, an initial 3D model 165 is a sphere that is updatedaccording to image space losses 174 to produce a reduced resolution 3Dmodel 170. As shown in FIG. 1C, the reduced resolution 3D model 170 maybe iteratively updated according to the image space losses 174 tooptimize the reduced resolution 3D model 170 that, when rendered,produces images 172 that more closely resemble reference images 176 of areference 3D model 175.

The rendering pipeline 160 used to process the reference 3D model 175and produce the reference images 176 may be the same or differentcompared with the rendering pipeline 100. In an embodiment, therendering pipeline 100 is differentiable and can produce images from 3Dgeometry in a forward operating pass and can also update the 3D geometryin a backward operating pass. The rendering operations may includerasterization, ray tracing, and path tracing. In contrast, the renderingpipeline 160 may perform one or more operations that are notdifferentiable. However, both rendering pipelines 100 and 160 generatethe images 172 and the reference images 176 according to the sameenvironmental conditions 178. In an embodiment, neither the renderingpipeline 100 nor the rendering pipeline 160 renders shadows or otherglobal effects. Note that effects that are visible in the referenceimages 176 will influence updates to the reduced resolution 3D model170. For example, when the reference images 176 are rendered withambient occlusion or path tracing enabled, the resulting effects will beencoded into material parameters of the reduced resolution 3D model 170.Therefore, geometry of the reduced resolution 3D model 170 mayreasonably approximate geometry of the reference 3D model 175 regardlessof whether the rendering pipeline 100 has ambient occlusion and/or pathtracing enabled.

The rendering pipeline 160 may be considered as a black box because theonly information communicated from the rendering pipeline 160 and thereduced resolution 3D model 170 are the reference images that are usedby the image space loss unit 120 to compute the image-domain loss.Therefore, the geometry representation defining the reduced resolution3D model 170 and reference 3D model 175 may be different. For example,the reference 3D model 175 may be represented as a signed distance fieldwhile the initial 3D model 165 and the reduced resolution 3D model 170are represented as triangle meshes. The different parameters for thereduced resolution 3D model 170 are jointly optimized, allowingcooperation between different parameter types affecting shape andappearance (e.g., vertex locations and normal and displacement mapsand/or tessellation and textures).

The image space loss unit 120 computes the image space losses 174 forthe images 172 and reference images 176. In an embodiment, θ denotes theparameters of the reduced 3D model representation (e.g., mesh vertexpositions and spatially varying material parameters). The renderedimages 172 I_(θ)(c,l) is a function of θ, camera, c, and light, l. Thereference rendering pipeline 160 is another function I_(ref)(c,l),parameterized by the camera and light. Given an image space lossfunction L, the empirical risk

$\begin{matrix}{\underset{\theta}{\arg\min}{{\mathbb{E}}_{c,l}\left\lbrack {{L\left( {I_{\theta}\left( {c,l} \right)} \right)},\left( {I_{ref}\left( {c,l} \right)} \right)} \right\rbrack}} & {{Eq}.(1)}\end{matrix}$is minimized using stochastic gradient descent based on gradients w.r.t.the parameters, ∂L/∂θ, which are obtained through differentiablerendering.

In an embodiment, a Laplacian regularizer may be used to as anadditional loss term to promote well-formed meshes, using the vertexpositions of the optimized reduced resolution 3D model 170. Particularlywhen gradients are large, the Laplacian regularizer may be used to keepthe mesh surface intact. A uniformly-weighted differential δ_(i) ofvertex v_(i) is given by

${\delta_{i} = {v_{i} - {\frac{1}{❘N_{i}❘}{\sum\limits_{j \in N_{i}}{v}_{j}}}}},$where N_(i) is the one-ring neighborhood of vertex v_(i). In anembodiment, a Laplacian regularizer term is given by

$\begin{matrix}{{L_{\delta} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}{{\delta_{i} - \delta_{i}^{\prime}}}^{2}}}},} & {{Eq}.(2)}\end{matrix}$where δ_(i)′ is the uniformly-weighted differential of the input mesh(i.e., initial 3D model 165). When the input mesh is a poorapproximation, e.g., a sphere, an absolute regularizer may be used, withδ_(i)′=0. In an embodiment, the combined objective function is:L _(opt) =L _(image)+λ_(t) L _(δ)  Eq (3)where λ_(t) is the regularization weight that depends on the currentoptimization iteration t. λ_(t) may be gradually reduced duringoptimization according to λ_(t)=(λ_(t-1)−λ_(min))·10^(−kt)+λ_(min). Inan embodiment, k=10⁻⁶, and λ_(min) is chosen as 2% of the initialweight, λ₀. The uniform Laplacian regularizer depends on tessellation,whereas the image-domain loss does not. Hence, the image space lossshould be balanced against the Laplacian loss when the meshes havegreatly varying primitive counts. The initial weight, λ₀, can either bespecified by the user or by a simple heuristic. For example, theLaplacian error may be evaluated at the start of optimization, and setas λ₀=0.25 L_(image)/L_(δ).

In non-linear optimization, good initial guesses may have a dramaticeffect on the speed of convergence and eventual quality of the resultingreduced resolution 3D model 170. When a high-resolution mesh of thereference 3D model 175 is available, mesh decimation tools may be usedto produce the initial 3D model 165. In some cases, e.g., when bakingfoliage as billboard clouds (as shown in FIG. 2D), prior domainknowledge may be used to explicitly specify a suitable initial mesh.However, starting from a tessellated sphere, as shown in FIGS. 1A and1C, often yields surprisingly good results. Similarly, if available,texture maps from a reference scene or the reference 3D model 175 may beused as an initial guess for material parameters. In an embodiment,randomly initialized texture maps are used for the initial 3D model 165.

The appearance driven automatic 3D modeling system 150 may be used formany purposes, including, but not limited to joint simplification ofshape and appearance, pre-filtering shape and appearance to reducealiasing, geometric simplification of skinned character animation,approximation of aggregate geometry, and 3D mesh extraction fromimplicit surfaces. Simplifying complex reference 3D models (e.g.,assets) with minimal loss in visual fidelity is a straightforwardapplication. As further described in conjunction with FIGS. 2A, 2B, and2C, three variants of model simplification demonstrate jointoptimization over different combinations of shape and appearance: normalmap baking, joint simplification that also accounts for surfacereflectance, and approximating complex meshes with displacement mapsapplied on a coarse base domain.

As an initial example, the initial 3D model 105 may comprise a spherewith 3 k triangles and the shape and a tangent-space normal map may beoptimized to produce the reduced resolution 3D model 110 shown in FIG.1A that approximates a highly detailed reference mesh with 735 ktriangles. Besides the normal vectors, the material for the reducedresolution 3D model 110 is otherwise fixed as diffuse uniform gray.While some high-frequency detail is missing from the skull in therendered 3D model representation 115, the result is nonethelessencouraging, considering that the optimization process may be entirelyautomatic and uses no direct information about the reference model.

In addition to normal maps, displacement mapping is an increasinglypopular technique for representing complex shapes in real-time settings.Displacement mapping achieves a compact representation by tessellating acoarse base mesh on-the-fly and displacing the resulting vertices in thedirection of the interpolated surface normal vector by amounts read froma displacement map texture. The appearance driven automatic 3D modelingsystem 150 enables the use of displacement maps for approximatinggeometry by simply implementing the tessellation and displacement stepsin the forward processing performed by the rendering pipeline 100.

FIG. 2A illustrates a conceptual diagram of normal vector anddisplacement mapping for a 3D model, in accordance with an embodiment.In an embodiment, the appearance driven automatic 3D modeling system 150jointly optimizes a base mesh, displacement map, and normal map of aninitial 3D model 205 to match the appearance of a 370 k trianglereference 3D model 225 of a dancer. The dancer model is courtesy of theSmithsonian® 3D Digitization project. The 1 k triangle initial 3D model205 is a decimated mesh generated from the reference 3D model 225. Thedancer model presents a complex optimization problem, and the initial 3Dmodel 205 provides a coarsely tessellated base mesh with displacementconstrained to the normal direction. Still, the appearance of both a 64k triangle reduced resolution 3D model 210 comprising a displacement mapwithout a normal map and a 64 k triangle reduced resolution 3D model 215comprising a displacement map with a normal map, closely match thereference 3D model 225. Interestingly, some small details in an inset227 portion of the reference 3D model 225 are baked into the normal mapof the reduced resolution 3D model 215, even though the small detailscould be easily represented by displacement. A portion of the renderedinitial 3D model 205 corresponding to the inset 227 is shown in 220. Aportion of the reduced resolution 3D model 210 (displacement withoutnormals) corresponding to the inset 227 is shown in 222. A portion ofthe reduced resolution 3D model 215 (displacement with normals)corresponding to the inset 227 is shown in 224.

The reduced resolution 3D model 215 is obtained by jointly optimizingthe pre-tessellation shape of the initial 3D model 205, the normal map,and the displacement map. The optimization yields a natural “division oflabor” between the representations: the initial 3D model 205 is a basemesh that models the overall shape, the displacement map modelsmid-scale detail, and the finest detail that is not representable by thedisplaced surface is captured by the normal map. While the reducedresolution 3D models 210 and 215 shown in FIG. 2A are generated for asingle, fixed tessellation level, dynamic tessellation may be used forlevel-of-detail selection. Additionally, a displacement mapped mesh maybe optimized to look good under multiple levels of tessellation.

Much of the difficulty in appearance capture originates from the desireto limit the acquisition effort for the user. Exhaustively measuringreal-world appearance under all lighting and view directions isprohibitively expensive for most purposes. In contrast, the reference 3Dmodel 225 can be rendered in as many viewing and lighting conditions asneeded to generate the reduced resolution 3D models 210 and 215. Thecomputational complexity is therefore reduced, and direct end-to-endoptimization over the material parameters and vertex positions ispossible using the visual similarity loss. In an embodiment, theappearance driven automatic 3D modeling system 150 generates the reducedresolution 3D models 210 and 215 comprising triangle meshes andmaterials that can be rendered in real-time by a standard 3D engine.

Appearance capture can be framed as seeking a digital asset (e.g., aspatially-varying BRDF map and a mesh) whose renderings visually matchsome real-world object. Appearance capture is conceptually similar toappearance driven automatic 3D modeling, with the exception that forappearance driven automatic 3D modeling the reference 3D models can beother digital assets.

FIG. 2B illustrates a conceptual diagram of normal vector and newlighting scenario for a 3D model, in accordance with an embodiment.Compared with the dancer reference 3D model 225 in FIG. 1B, a reference3D model 245 is rendered with a uniform, highly specular gold material.A physically-based shading model with one diffuse lobe and one isotropicspecular lobe, as is commonly used in modern game engines, may beimplemented by the rendering pipeline 100. Joint shape and appearancesimplification may be used to generate a reduced resolution 3D model 240of the reference 3D model 245. Initial 3D model 235 includes 1 ktriangles and the reference 3D model 245 includes 370 k triangles. Thereduced resolution 3D model 240 includes 1 k triangles—only 0.3% of thetriangles that are included in the reference 3D model 245. The reducedresolution model 240 is rendered in a new lighting scenariodemonstrating that the materials learned during optimization generalizeto new lighting conditions that were not used during optimization.

In contrast with the reduced resolution 3D model 240 shown in FIG. 2Athat comprises a spatially uniform BRDF, the reduced resolution 3D model240 is endowed with a normal map and a spatially-varying BRDF (SVBRDF).As shown by parameters for the dancer's head: mesh 230, diffuse map 232,specular map 234, and normal vectors 236, geometric detail not presentin the reduced resolution 3D model 240 becomes approximated in thevarious spatially varying material parameters. Shape and appearance maybe jointly optimized under random views and point light directions. Eventhough a single point light is used during optimization, the resultingreduced resolution 3D model 240 generalizes to new lighting conditions.In other words, the resulting reduced resolution 3D model 240generalizes to different environmental conditions. Because appearancedriven automatic 3D modeling optimizes based on visual differences, thesilhouette of the reduced resolution 3D model 240 closely matches thetrue silhouette of the reference 3D model 245, accurate normal vectorsare computed, and the shaded appearance of the reference 3D model 240 iscaptured.

In an embodiment, automatic cleanup of unsuccessful mesh decimationoperations performed by other applications may be performed using theappearance driven automatic 3D modeling system 150. For example,artifacts in a decimated mesh of a reference 3D model such as decreasedvolume, detached geometric elements, incorrect texturing, andself-intersecting geometry may be corrected. The decimated mesh is usedan initial 3D model, images of the initial 3D model and reducedresolution 3D model are rendered and compared with rendered images ofthe reference 3D model to produce a version of the decimated mesh havingfewer artifacts compared with the decimated mesh.

As previously described, a goal is to create faithful representations ofcomplex assets (e.g., 3D reference models) with reduced resolution(e.g., triangle counts). A closely related goal is to find efficientlyrenderable approximations to original assets that require a substantialamount of supersampling, due to their complexity, to produce alias-freeimages. In the context of the following description, joint pre-filteringof shape and appearance is used to refer to a process of producing suchefficiently renderable approximations of reference 3D models. Reducedresolution 3D models produced using joint pre-filtering of shape andappearance have the property that they reproduce, when rendered at onlyone sample-per-pixel (1 spp), the appearance of assets that requirepotentially hundreds of samples per pixel for alias-free reproduction.During optimization, a smaller target image resolution is typicallyspecified for the rendered images and the reference images should berendered with enough supersampling to ensure lack of aliasing. In anembodiment, the shading model used by the rendering pipeline 100comprises one diffuse lobe and one specular lobe, and the mesh shape andthe material parameters are adjusted during optimization so that therendered images match highly supersampled reference images.

FIG. 2C illustrates a conceptual diagram of appearance pre-filtering fora 3D model, in accordance with an embodiment. Reference 3D models 242and 252 and reduced resolution 3D models 244 and 254, are rendered using1spp. For comparison, reference 3D models 246 and 256 are rendered using256spp. Shape and appearance can be jointly pre-filtered for one or morespecific rendering resolutions. The reduced resolution 3D model 244 isoptimized for an image resolution of 64×64 pixels based on referenceimages of the reference 3D model 246. The reduced resolution 3D model254 is optimized for a resolution of 512×512 pixels based on referenceimages of the reference 3D model 256. Pre-filtering can be achieved inthe appearance driven automatic 3D modeling system 150 by updating therendering pipeline used for the reference 3D model 160 to renderreference images using multiple samples and antialiasing.

The reduced resolution 3D model 244 which is pre-filtered for the lowerresolution of 64×64 pixels has, as one would expect, considerablysmoother normal vectors compared with the reduced resolution 3D model254 that account, together with the specular map, for the effect ofaveraging inherent in supersampling. Similarly, compared with thereduced resolution 3D model 254, the reduced resolution 3D model 244 isgeometrically smoothed. When rendered at the intended resolution, thereduced resolution 3D models 244 and 254 match the appearance of therespective reference 3D models 242 and 252 well, with no apparentaliasing. The appearance of the supersampled reference 3D models 245 and256 rendered at 256spp are achieved by the respective reduced resolution3D models 244 and 254 even though the reduced resolution 3D models 244and 254 are rendered using 1spp. Thus, reduced resolution 3D models 244and 254 may be used to produce images at a fraction of the renderingcost compared with the anti-aliased reference 3D models 246 and 256.

To obtain an appropriate amount of pre-filtering at different renderingresolutions (i.e., rendering distances), it is not sufficient tooptimize for one resolution only. For materials, it is easy to optimizefor multiple resolutions by treating each mip map level separately usingper-mip map level parameters in a set of parameters for the materials.Such resolution-specific material representations may be used byexisting trilinear texture lookup mechanisms for automatic interpolatebetween levels. Even though linear interpolation between materialparameters is not generally correct, the optimization process will finda representation that, assuming trilinear texture fetches will be used,matches the reference images as well as possible. For geometry, multiplesets of vertex positions may be stored and at least one particular setof vertex positions may be selected based on distance to mesh, averageprojected edge length, or a similar heuristic. As with mip-mapping,linear interpolation between levels can be used to eliminate poppingartifacts.

The pre-filtering technique has the benefit that no changes are neededfor a typical game engine to render the reduced resolution models andthere is no runtime overhead. The approach is flexible, as any targetsurface and material representation of the reference 3D model is treatedin a unified manner: only the final visual appearance is observed, andit does not matter what combination of, e.g., mesh shape, displacement,normal, and material parameters produced the rendered images.

When the reference 3D model changes position or shape over time, such asin an animation, an animated and articulated reduced resolution 3D modelmay be generated. In an embodiment, the parameters may include vertexpositions, skinning weights, normal maps, and material properties thatvary over time. More precisely, given a high-resolution reference 3Dmodel animated by skeletal subspace deformation (SSD), optimization maybe performed over bind-pose vertex positions, normal vectors, SVBRDF,and skinning weights (bone-vertex attachments) of an initial 3D model inan attempt to replicate the appearance of the reference animation. Incontrast to simplifying the reference 3D model (e.g., character) in thebind pose (T-pose) only, appearance driven automatic 3D modeling holdspromise for being able to negotiate compromises to distribute the errorevenly among the frames by adjusting the geometry, skinning weights, andmaterials appropriately.

Compared with other applications, when SSD is used, transformed vertexpositions are blended using the skinning weights, a simple linearoperation. In an embodiment, an assumption is made that time-varyingbone transformations are known and are therefore treated as constantsduring optimization. In an embodiment, appearance driven, end-to-endjoint optimization of both bone transformations and skinning weights,together with geometry and material parameters is performed by theappearance driven automatic 3D modeling system 150.

In another application, shape and appearance of aggregate geometryincluded in detailed 3D scenes may be approximated by the appearancedriven automatic 3D modeling system 150. Stochastic aggregate geometry,such as foliage, are particularly difficult to simplify: as the overallappearance emerges from the combined effect of many small, disjointcomponents, techniques such as mesh decimation are ineffective. Aconventional simplification technique randomly removes a subset of thegeometric elements and alters the remaining elements based on a knownscene graph, to preserve the overall appearance of a scene. Instead ofstochastically pruning the procedural scene graph as is done by theconventional simplification, appearance driven automatic 3D modelingreplaces the complex geometries with textured geometry that is simple(e.g., quads). The simple geometry provides the initial 3D model, andthe material parameters, shape, and transparency may be jointlyoptimized based on visual loss of rendered images.

FIG. 2D illustrates a conceptual diagram of simplification of aggregategeometry for a 3D model, in accordance with an embodiment. Foliage isapproximated with low-poly proxy geometry and textures. Reference 3Dmodel geometry that is used to produce rendered reference 3D model 246comprises complex shapes for the foliage, whereas initial 3D modelgeometry 242 comprises simple quadrilaterals using just a fraction(3.66%) of the triangle count (256 quads or 512 triangles) compared withthe reference 3D model geometry (14 k triangles). As shown in FIG. 2D,the quadrilaterals for the initial 3D model geometry 246 are randomlyplaced in 3D.

In an embodiment, rendered reduced 3D model 244 is produced byoptimizing the initial 3D model geometry 242 and associated parameters,including a learned texture 248, using differentiable operationsimplemented by the rendering pipeline 100. During optimization, vertexpositions of the initial 3D model geometry 242 are adjusted and materialvalues are also adjusted to optimize the learned texture 248. In anembodiment, squared L₂ is used as the objective function by the imagespace loss unit 120. Compared to rendered reference 3D model geometry246, the appearance of the scene is accurately approximated by therendered reduced 3D model 244. In addition to colors, the texturesinclude alpha values that effectively shape the reduced 3D modelgeometry by making portions of the geometry transparent. In addition togeometry, the reference 3D model also includes spatially varyingmaterials and textures with alpha values.

FIG. 3 illustrates a block diagram of the rendering pipeline 100 shownin FIGS. 1A and 1C suitable for use in implementing some embodiments ofthe present disclosure. It should be understood that this and otherarrangements described herein are set forth only as examples. Otherarrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory. Furthermore, persons of ordinary skill inthe art will understand that any system that performs the operations ofthe rendering pipeline 100 for the appearance driven automatic 3Dmodeling system 150 is within the scope and spirit of embodiments of thepresent disclosure.

The rendering pipeline 100 optimizes the reduced resolution 3D model110. In an embodiment, an initial guess for the reduced resolution 3Dmodel 110, such as the initial 3D model 105 is processed and updated,according to the image space losses 122, to produce the reducedresolution 3D model 110. The reduced resolution 3D model 110 may beiteratively updated according to the image space losses 122.

The rendering pipeline 100 comprises multiple processing units includinga tangent space construction unit 300, an animation and skinning unit310, a displacement mapping unit 320, a differentiable rasterizer 330,and a differed shading unit 340. In other embodiment, one or more of theprocessing units within the rendering pipeline 100 may be omitted or oneor more additional units maybe included. In an embodiment, the renderingpipeline 100 comprises modular, configurable, and programmableprocessing units to enable easy construction of potentially complexcustom rendering pipelines. Each one of the processing units performsdifferentiable operations. One or more of the processing units performsoperations related to the various parameters of the initial and reducedresolution 3D models, such as vertex positions, normal (vector) maps,displacement maps, texture maps, skinning weights, and the like.

To optimize tangent space normal maps on deforming geometry, a tangentframe must be differentiable and dynamically updated to reflect anychange in vertex position. In an embodiment, the tangent spaceconstruction unit 300 computes smooth vertex normals and derives tangentand bi-tangent vectors from the vertex positions and texturecoordinates. Using mesh-derived smooth normals is not necessarily alimitation because creases or other sharp features can be handled by thenormal map.

Skinning may be supported by optimizing skinning weights and/or a bonetransform matrix of animated meshes. A differentiable skinning operatoris computed by the animation and skinning unit 310 according to:v _(i) ^(s) =

w _(ib) M _(b) v _(i)  Eq. (4)where

is the set of bones, M_(b) is the bone transform matrix for the currentframe, and w_(ib) is the skinning weight of bone b influencing vertexv_(i). Weights are typically stored using a sparse indexedrepresentation, but the full dense skinning operator may be implementedto support any vertex-bone association during optimization.

For displacement mapping, the representation of the initial 3D model 105is a coarse base mesh and scalar displacement map. The mesh issubdivided, and the displacement map is used to displace the tessellatedvertices along the interpolated normal direction. Geometry may betessellated using edge-midpoint subdivision, where each triangle issplit into four new triangles by inserting a new vertex along each edge.In an embodiment, the tessellation operation is made differentiable byselecting a constant tessellation factor and precomputing the topologyof the tessellated mesh before optimization. The position of each vertexcreated by the tessellation is recomputed every iteration to ensure thatgradients are propagated correctly.

In an embodiment, the displacement mapping unit 320 displaces eachvertex according tov _(i) ^(d) =v _(i)+tex2d(t _(i))·n _(i),  Eq. (5)where v_(i) is the original tessellated vertex position, n_(i) is theinterpolated normal, tex2d is a texture lookup operation, and t_(i) isthe texture coordinate. Each of the tangent space construction unit 300,animation and skinning unit 310, and displacement mapping unit 320operates on a mesh defined by vertices.

The differentiable rasterizer 330 converts the mesh geometry andparameters into attributes for interpolation and the deferred shadingunit 340 computes a final color for each pixel of images 122 byperforming interpolation, texture lookup, and/or antialiasingoperations.

The rendering pipeline 100 receives image space losses 122 that, in anembodiment, indicate how the color of each pixel of the images 122affects the loss. In an embodiment, backwards propagation through eachprocessing unit in the rendering pipeline 100 computes gradients thatare output to the upstream processing unit. When propagating thegradients backwards through the rendering pipeline 100, the gradient ofthe output for each computation step is known (i.e., how changes in theoutput values of each computation step will affect the loss), so thatthe gradients of the inputs to the computation step may be determined(i.e., how changes in the input values of each computation step willaffect the loss).

After the image space losses 122 are propagated backwards through theentire rendering pipeline 100, it is possible to quantify how changingthe initial 3D model 105 and the reduced resolution 3D model 110 affectsthe image space losses 122, and the parameters can be adjusted in thedirection that should reduce the image space losses 122. The gradientsthat are computed during backpropagation indicate the effect that movingthe vertex positions and adjusting other parameters has on the images112. The rendering pipeline 100 provides updates and adjusts the reducedresolution 3D model 110 to reduce the image space losses 122.

The appearance driven automatic 3D modeling system 150 enablesoptimization of a 3D model comprising the shape and appearance of aparticular 3D scene or object for individual frames or an animation.Compared with a reference 3D model, the optimized (reduced resolution)3D model is a lower resolution 3D model that can be rendered in lesstime. More specifically, the optimized 3D model may include fewergeometric primitives compared with the reference 3D model. The optimized3D model may be used to improve the performance of a game or otherreal-time application that is limited by rendering speed. Optimizationof the 3D model may be used to perform shape and appearancepre-filtering to improve image quality. More specifically, jointpre-filtering of shape and appearance may be used to produce alias-freeimages, even when rendering using only one sample per pixel. Theappearance driven automatic 3D modeling system 150 may also be used toconvert between different model representations. Real-time and/orproduction rendering applications (e.g., feature film, architecturalvisualization, etc.) may benefit from the improved image quality.

Parallel Processing Architecture

FIG. 4 illustrates a parallel processing unit (PPU) 400, in accordancewith an embodiment. The PPU 400 may be used to implement the appearancedriven automatic 3D modeling system 150. The PPU 400 may be used toimplement one or more of the rendering pipeline 100, image space lossunit 120, or rendering pipeline 160. In an embodiment, a processor suchas the PPU 400 may be configured to implement a neural network model.The neural network model may be implemented as software instructionsexecuted by the processor or, in other embodiments, the processor caninclude a matrix of hardware elements configured to process a set ofinputs (e.g., electrical signals representing values) to generate a setof outputs, which can represent activations of the neural network model.In yet other embodiments, the neural network model can be implemented asa combination of software instructions and processing performed by amatrix of hardware elements. Implementing the neural network model caninclude determining a set of parameters for the neural network modelthrough, e.g., supervised or unsupervised training of the neural networkmodel as well as, or in the alternative, performing inference using theset of parameters to process novel sets of inputs.

In an embodiment, the PPU 400 is a multi-threaded processor that isimplemented on one or more integrated circuit devices. The PPU 400 is alatency hiding architecture designed to process many threads inparallel. A thread (e.g., a thread of execution) is an instantiation ofa set of instructions configured to be executed by the PPU 400. In anembodiment, the PPU 400 is a graphics processing unit (GPU) configuredto implement a graphics rendering pipeline for processingthree-dimensional (3D) graphics data in order to generatetwo-dimensional (2D) image data for display on a display device. Inother embodiments, the PPU 400 may be utilized for performinggeneral-purpose computations. While one exemplary parallel processor isprovided herein for illustrative purposes, it should be strongly notedthat such processor is set forth for illustrative purposes only, andthat any processor may be employed to supplement and/or substitute forthe same.

One or more PPUs 400 may be configured to accelerate thousands of HighPerformance Computing (HPC), data center, cloud computing, and machinelearning applications. The PPU 400 may be configured to acceleratenumerous deep learning systems and applications for autonomous vehicles,simulation, computational graphics such as ray or path tracing, deeplearning, high-accuracy speech, image, and text recognition systems,intelligent video analytics, molecular simulations, drug discovery,disease diagnosis, weather forecasting, big data analytics, astronomy,molecular dynamics simulation, financial modeling, robotics, factoryautomation, real-time language translation, online search optimizations,and personalized user recommendations, and the like.

As shown in FIG. 4 , the PPU 400 includes an Input/Output (I/O) unit405, a front end unit 415, a scheduler unit 420, a work distributionunit 425, a hub 430, a crossbar (Xbar) 470, one or more generalprocessing clusters (GPCs) 450, and one or more memory partition units480. The PPU 400 may be connected to a host processor or other PPUs 400via one or more high-speed NVLink 410 interconnect. The PPU 400 may beconnected to a host processor or other peripheral devices via aninterconnect 402. The PPU 400 may also be connected to a local memory404 comprising a number of memory devices. In an embodiment, the localmemory may comprise a number of dynamic random access memory (DRAM)devices. The DRAM devices may be configured as a high-bandwidth memory(HBM) subsystem, with multiple DRAM dies stacked within each device.

The NVLink 410 interconnect enables systems to scale and include one ormore PPUs 400 combined with one or more CPUs, supports cache coherencebetween the PPUs 400 and CPUs, and CPU mastering. Data and/or commandsmay be transmitted by the NVLink 410 through the hub 430 to/from otherunits of the PPU 400 such as one or more copy engines, a video encoder,a video decoder, a power management unit, etc. (not explicitly shown).The NVLink 410 is described in more detail in conjunction with FIG. 5B.

The I/O unit 405 is configured to transmit and receive communications(e.g., commands, data, etc.) from a host processor (not shown) over theinterconnect 402. The I/O unit 405 may communicate with the hostprocessor directly via the interconnect 402 or through one or moreintermediate devices such as a memory bridge. In an embodiment, the I/Ounit 405 may communicate with one or more other processors, such as oneor more the PPUs 400 via the interconnect 402. In an embodiment, the I/Ounit 405 implements a Peripheral Component Interconnect Express (PCIe)interface for communications over a PCIe bus and the interconnect 402 isa PCIe bus. In alternative embodiments, the I/O unit 405 may implementother types of well-known interfaces for communicating with externaldevices.

The I/O unit 405 decodes packets received via the interconnect 402. Inan embodiment, the packets represent commands configured to cause thePPU 400 to perform various operations. The I/O unit 405 transmits thedecoded commands to various other units of the PPU 400 as the commandsmay specify. For example, some commands may be transmitted to the frontend unit 415. Other commands may be transmitted to the hub 430 or otherunits of the PPU 400 such as one or more copy engines, a video encoder,a video decoder, a power management unit, etc. (not explicitly shown).In other words, the I/O unit 405 is configured to route communicationsbetween and among the various logical units of the PPU 400.

In an embodiment, a program executed by the host processor encodes acommand stream in a buffer that provides workloads to the PPU 400 forprocessing. A workload may comprise several instructions and data to beprocessed by those instructions. The buffer is a region in a memory thatis accessible (e.g., read/write) by both the host processor and the PPU400. For example, the I/O unit 405 may be configured to access thebuffer in a system memory connected to the interconnect 402 via memoryrequests transmitted over the interconnect 402. In an embodiment, thehost processor writes the command stream to the buffer and thentransmits a pointer to the start of the command stream to the PPU 400.The front end unit 415 receives pointers to one or more command streams.The front end unit 415 manages the one or more streams, reading commandsfrom the streams and forwarding commands to the various units of the PPU400.

The front end unit 415 is coupled to a scheduler unit 420 thatconfigures the various GPCs 450 to process tasks defined by the one ormore streams. The scheduler unit 420 is configured to track stateinformation related to the various tasks managed by the scheduler unit420. The state may indicate which GPC 450 a task is assigned to, whetherthe task is active or inactive, a priority level associated with thetask, and so forth. The scheduler unit 420 manages the execution of aplurality of tasks on the one or more GPCs 450.

The scheduler unit 420 is coupled to a work distribution unit 425 thatis configured to dispatch tasks for execution on the GPCs 450. The workdistribution unit 425 may track a number of scheduled tasks receivedfrom the scheduler unit 420. In an embodiment, the work distributionunit 425 manages a pending task pool and an active task pool for each ofthe GPCs 450. As a GPC 450 finishes the execution of a task, that taskis evicted from the active task pool for the GPC 450 and one of theother tasks from the pending task pool is selected and scheduled forexecution on the GPC 450. If an active task has been idle on the GPC450, such as while waiting for a data dependency to be resolved, thenthe active task may be evicted from the GPC 450 and returned to thepending task pool while another task in the pending task pool isselected and scheduled for execution on the GPC 450.

In an embodiment, a host processor executes a driver kernel thatimplements an application programming interface (API) that enables oneor more applications executing on the host processor to scheduleoperations for execution on the PPU 400. In an embodiment, multiplecompute applications are simultaneously executed by the PPU 400 and thePPU 400 provides isolation, quality of service (QoS), and independentaddress spaces for the multiple compute applications. An application maygenerate instructions (e.g., API calls) that cause the driver kernel togenerate one or more tasks for execution by the PPU 400. The driverkernel outputs tasks to one or more streams being processed by the PPU400. Each task may comprise one or more groups of related threads,referred to herein as a warp. In an embodiment, a warp comprises 32related threads that may be executed in parallel. Cooperating threadsmay refer to a plurality of threads including instructions to performthe task and that may exchange data through shared memory. The tasks maybe allocated to one or more processing units within a GPC 450 andinstructions are scheduled for execution by at least one warp.

The work distribution unit 425 communicates with the one or more GPCs450 via XBar 470. The XBar 470 is an interconnect network that couplesmany of the units of the PPU 400 to other units of the PPU 400. Forexample, the XBar 470 may be configured to couple the work distributionunit 425 to a particular GPC 450. Although not shown explicitly, one ormore other units of the PPU 400 may also be connected to the XBar 470via the hub 430.

The tasks are managed by the scheduler unit 420 and dispatched to a GPC450 by the work distribution unit 425. The GPC 450 is configured toprocess the task and generate results. The results may be consumed byother tasks within the GPC 450, routed to a different GPC 450 via theXBar 470, or stored in the memory 404. The results can be written to thememory 404 via the memory partition units 480, which implement a memoryinterface for reading and writing data to/from the memory 404. Theresults can be transmitted to another PPU 400 or CPU via the NVLink 410.In an embodiment, the PPU 400 includes a number U of memory partitionunits 480 that is equal to the number of separate and distinct memorydevices of the memory 404 coupled to the PPU 400. Each GPC 450 mayinclude a memory management unit to provide translation of virtualaddresses into physical addresses, memory protection, and arbitration ofmemory requests. In an embodiment, the memory management unit providesone or more translation lookaside buffers (TLBs) for performingtranslation of virtual addresses into physical addresses in the memory404.

In an embodiment, the memory partition unit 480 includes a RasterOperations (ROP) unit, a level two (L2) cache, and a memory interfacethat is coupled to the memory 404. The memory interface may implement32, 64, 128, 1024-bit data buses, or the like, for high-speed datatransfer. The PPU 400 may be connected to up to Y memory devices, suchas high bandwidth memory stacks or graphics double-data-rate, version 5,synchronous dynamic random access memory, or other types of persistentstorage. In an embodiment, the memory interface implements an HBM2memory interface and Y equals half U. In an embodiment, the HBM2 memorystacks are located on the same physical package as the PPU 400,providing substantial power and area savings compared with conventionalGDDR5 SDRAM systems. In an embodiment, each HBM2 stack includes fourmemory dies and Y equals 4, with each HBM2 stack including two 128-bitchannels per die for a total of 8 channels and a data bus width of 1024bits.

In an embodiment, the memory 404 supports Single-Error CorrectingDouble-Error Detecting (SECDED) Error Correction Code (ECC) to protectdata. ECC provides higher reliability for compute applications that aresensitive to data corruption. Reliability is especially important inlarge-scale cluster computing environments where PPUs 400 process verylarge datasets and/or run applications for extended periods.

In an embodiment, the PPU 400 implements a multi-level memory hierarchy.In an embodiment, the memory partition unit 480 supports a unifiedmemory to provide a single unified virtual address space for CPU and PPU400 memory, enabling data sharing between virtual memory systems. In anembodiment the frequency of accesses by a PPU 400 to memory located onother processors is traced to ensure that memory pages are moved to thephysical memory of the PPU 400 that is accessing the pages morefrequently. In an embodiment, the NVLink 410 supports addresstranslation services allowing the PPU 400 to directly access a CPU'spage tables and providing full access to CPU memory by the PPU 400.

In an embodiment, copy engines transfer data between multiple PPUs 400or between PPUs 400 and CPUs. The copy engines can generate page faultsfor addresses that are not mapped into the page tables. The memorypartition unit 480 can then service the page faults, mapping theaddresses into the page table, after which the copy engine can performthe transfer. In a conventional system, memory is pinned (e.g.,non-pageable) for multiple copy engine operations between multipleprocessors, substantially reducing the available memory. With hardwarepage faulting, addresses can be passed to the copy engines withoutworrying if the memory pages are resident, and the copy process istransparent.

Data from the memory 404 or other system memory may be fetched by thememory partition unit 480 and stored in the L2 cache 460, which islocated on-chip and is shared between the various GPCs 450. As shown,each memory partition unit 480 includes a portion of the L2 cacheassociated with a corresponding memory 404. Lower level caches may thenbe implemented in various units within the GPCs 450. For example, eachof the processing units within a GPC 450 may implement a level one (L1)cache. The L1 cache is private memory that is dedicated to a particularprocessing unit. The L2 cache 460 is coupled to the memory interface 470and the XBar 470 and data from the L2 cache may be fetched and stored ineach of the L1 caches for processing.

In an embodiment, the processing units within each GPC 450 implement aSIMD (Single-Instruction, Multiple-Data) architecture where each threadin a group of threads (e.g., a warp) is configured to process adifferent set of data based on the same set of instructions. All threadsin the group of threads execute the same instructions. In anotherembodiment, the processing unit implements a SIMT (Single-Instruction,Multiple Thread) architecture where each thread in a group of threads isconfigured to process a different set of data based on the same set ofinstructions, but where individual threads in the group of threads areallowed to diverge during execution. In an embodiment, a programcounter, call stack, and execution state is maintained for each warp,enabling concurrency between warps and serial execution within warpswhen threads within the warp diverge. In another embodiment, a programcounter, call stack, and execution state is maintained for eachindividual thread, enabling equal concurrency between all threads,within and between warps. When execution state is maintained for eachindividual thread, threads executing the same instructions may beconverged and executed in parallel for maximum efficiency.

Cooperative Groups is a programming model for organizing groups ofcommunicating threads that allows developers to express the granularityat which threads are communicating, enabling the expression of richer,more efficient parallel decompositions. Cooperative launch APIs supportsynchronization amongst thread blocks for the execution of parallelalgorithms. Conventional programming models provide a single, simpleconstruct for synchronizing cooperating threads: a barrier across allthreads of a thread block (e.g., the syncthreads( ) function). However,programmers would often like to define groups of threads at smaller thanthread block granularities and synchronize within the defined groups toenable greater performance, design flexibility, and software reuse inthe form of collective group-wide function interfaces.

Cooperative Groups enables programmers to define groups of threadsexplicitly at sub-block (e.g., as small as a single thread) andmulti-block granularities, and to perform collective operations such assynchronization on the threads in a cooperative group. The programmingmodel supports clean composition across software boundaries, so thatlibraries and utility functions can synchronize safely within theirlocal context without having to make assumptions about convergence.Cooperative Groups primitives enable new patterns of cooperativeparallelism, including producer-consumer parallelism, opportunisticparallelism, and global synchronization across an entire grid of threadblocks.

Each processing unit includes a large number (e.g., 128, etc.) ofdistinct processing cores (e.g., functional units) that may befully-pipelined, single-precision, double-precision, and/or mixedprecision and include a floating point arithmetic logic unit and aninteger arithmetic logic unit. In an embodiment, the floating pointarithmetic logic units implement the IEEE 754-2008 standard for floatingpoint arithmetic. In an embodiment, the cores include 64single-precision (32-bit) floating point cores, 64 integer cores, 32double-precision (64-bit) floating point cores, and 8 tensor cores.

Tensor cores configured to perform matrix operations. In particular, thetensor cores are configured to perform deep learning matrix arithmetic,such as GEMM (matrix-matrix multiplication) for convolution operationsduring neural network training and inferencing. In an embodiment, eachtensor core operates on a 4×4 matrix and performs a matrix multiply andaccumulate operation D=A×B+C, where A, B, C, and D are 4×4 matrices.

In an embodiment, the matrix multiply inputs A and B may be integer,fixed-point, or floating point matrices, while the accumulation matricesC and D may be integer, fixed-point, or floating point matrices of equalor higher bitwidths. In an embodiment, tensor cores operate on one,four, or eight bit integer input data with 32-bit integer accumulation.The 8-bit integer matrix multiply requires 1024 operations and resultsin a full precision product that is then accumulated using 32-bitinteger addition with the other intermediate products for a 8×8×16matrix multiply. In an embodiment, tensor Cores operate on 16-bitfloating point input data with 32-bit floating point accumulation. The16-bit floating point multiply requires 64 operations and results in afull precision product that is then accumulated using 32-bit floatingpoint addition with the other intermediate products for a 4×4×4 matrixmultiply. In practice, Tensor Cores are used to perform much largertwo-dimensional or higher dimensional matrix operations, built up fromthese smaller elements. An API, such as CUDA 9 C++ API, exposesspecialized matrix load, matrix multiply and accumulate, and matrixstore operations to efficiently use Tensor Cores from a CUDA-C++program. At the CUDA level, the warp-level interface assumes 16×16 sizematrices spanning all 32 threads of the warp.

Each processing unit may also comprise M special function units (SFUs)that perform special functions (e.g., attribute evaluation, reciprocalsquare root, and the like). In an embodiment, the SFUs may include atree traversal unit configured to traverse a hierarchical tree datastructure. In an embodiment, the SFUs may include texture unitconfigured to perform texture map filtering operations. In anembodiment, the texture units are configured to load texture maps (e.g.,a 2D array of texels) from the memory 404 and sample the texture maps toproduce sampled texture values for use in shader programs executed bythe processing unit. In an embodiment, the texture maps are stored inshared memory that may comprise or include an L1 cache. The textureunits implement texture operations such as filtering operations usingmip-maps (e.g., texture maps of varying levels of detail). In anembodiment, each processing unit includes two texture units.

Each processing unit also comprises N load store units (LSUs) thatimplement load and store operations between the shared memory and theregister file. Each processing unit includes an interconnect networkthat connects each of the cores to the register file and the LSU to theregister file, shared memory. In an embodiment, the interconnect networkis a crossbar that can be configured to connect any of the cores to anyof the registers in the register file and connect the LSUs to theregister file and memory locations in shared memory.

The shared memory is an array of on-chip memory that allows for datastorage and communication between the processing units and betweenthreads within a processing unit. In an embodiment, the shared memorycomprises 128 KB of storage capacity and is in the path from each of theprocessing units to the memory partition unit 480. The shared memory canbe used to cache reads and writes. One or more of the shared memory, L1cache, L2 cache, and memory 404 are backing stores.

Combining data cache and shared memory functionality into a singlememory block provides the best overall performance for both types ofmemory accesses. The capacity is usable as a cache by programs that donot use shared memory. For example, if shared memory is configured touse half of the capacity, texture and load/store operations can use theremaining capacity. Integration within the shared memory enables theshared memory to function as a high-throughput conduit for streamingdata while simultaneously providing high-bandwidth and low-latencyaccess to frequently reused data.

When configured for general purpose parallel computation, a simplerconfiguration can be used compared with graphics processing.Specifically, fixed function graphics processing units, are bypassed,creating a much simpler programming model. In the general purposeparallel computation configuration, the work distribution unit 425assigns and distributes blocks of threads directly to the processingunits within the GPCs 450. Threads execute the same program, using aunique thread ID in the calculation to ensure each thread generatesunique results, using the processing unit(s) to execute the program andperform calculations, shared memory to communicate between threads, andthe LSU to read and write global memory through the shared memory andthe memory partition unit 480. When configured for general purposeparallel computation, the processing units can also write commands thatthe scheduler unit 420 can use to launch new work on the processingunits.

The PPUs 400 may each include, and/or be configured to perform functionsof, one or more processing cores and/or components thereof, such asTensor Cores (TCs), Tensor Processing Units (TPUs), Pixel Visual Cores(PVCs), Ray Tracing (RT) Cores, Vision Processing Units (VPUs), GraphicsProcessing Clusters (GPCs), Texture Processing Clusters (TPCs),Streaming Multiprocessors (SMs), Tree Traversal Units (TTUs), ArtificialIntelligence Accelerators (AIAs), Deep Learning Accelerators (DLAs),Arithmetic-Logic Units (ALUs), Application-Specific Integrated Circuits(ASICs), Floating Point Units (FPUs), input/output (I/O) elements,peripheral component interconnect (PCI) or peripheral componentinterconnect express (PCIe) elements, and/or the like.

The PPU 400 may be included in a desktop computer, a laptop computer, atablet computer, servers, supercomputers, a smart-phone (e.g., awireless, hand-held device), personal digital assistant (PDA), a digitalcamera, a vehicle, a head mounted display, a hand-held electronicdevice, and the like. In an embodiment, the PPU 400 is embodied on asingle semiconductor substrate. In another embodiment, the PPU 400 isincluded in a system-on-a-chip (SoC) along with one or more otherdevices such as additional PPUs 400, the memory 404, a reducedinstruction set computer (RISC) CPU, a memory management unit (MMU), adigital-to-analog converter (DAC), and the like.

In an embodiment, the PPU 400 may be included on a graphics card thatincludes one or more memory devices. The graphics card may be configuredto interface with a PCIe slot on a motherboard of a desktop computer. Inyet another embodiment, the PPU 400 may be an integrated graphicsprocessing unit (iGPU) or parallel processor included in the chipset ofthe motherboard. In yet another embodiment, the PPU 400 may be realizedin reconfigurable hardware. In yet another embodiment, parts of the PPU400 may be realized in reconfigurable hardware.

Exemplary Computing System

Systems with multiple GPUs and CPUs are used in a variety of industriesas developers expose and leverage more parallelism in applications suchas artificial intelligence computing. High-performance GPU-acceleratedsystems with tens to many thousands of compute nodes are deployed indata centers, research facilities, and supercomputers to solve everlarger problems. As the number of processing devices within thehigh-performance systems increases, the communication and data transfermechanisms need to scale to support the increased bandwidth.

FIG. 5A is a conceptual diagram of a processing system 500 implementedusing the PPU 400 of FIG. 4 , in accordance with an embodiment. Theexemplary system 565 may be configured to implement the appearancedriven automatic 3D modeling system 150 and/or the method 130 shown inFIG. 1B. The processing system 500 includes a CPU 530, switch 510, andmultiple PPUs 400, and respective memories 404.

The NVLink 410 provides high-speed communication links between each ofthe PPUs 400. Although a particular number of NVLink 410 andinterconnect 402 connections are illustrated in FIG. 5B, the number ofconnections to each PPU 400 and the CPU 530 may vary. The switch 510interfaces between the interconnect 402 and the CPU 530. The PPUs 400,memories 404, and NVLinks 410 may be situated on a single semiconductorplatform to form a parallel processing module 525. In an embodiment, theswitch 510 supports two or more protocols to interface between variousdifferent connections and/or links.

In another embodiment (not shown), the NVLink 410 provides one or morehigh-speed communication links between each of the PPUs 400 and the CPU530 and the switch 510 interfaces between the interconnect 402 and eachof the PPUs 400. The PPUs 400, memories 404, and interconnect 402 may besituated on a single semiconductor platform to form a parallelprocessing module 525. In yet another embodiment (not shown), theinterconnect 402 provides one or more communication links between eachof the PPUs 400 and the CPU 530 and the switch 510 interfaces betweeneach of the PPUs 400 using the NVLink 410 to provide one or morehigh-speed communication links between the PPUs 400. In anotherembodiment (not shown), the NVLink 410 provides one or more high-speedcommunication links between the PPUs 400 and the CPU 530 through theswitch 510. In yet another embodiment (not shown), the interconnect 402provides one or more communication links between each of the PPUs 400directly. One or more of the NVLink 410 high-speed communication linksmay be implemented as a physical NVLink interconnect or either anon-chip or on-die interconnect using the same protocol as the NVLink410.

In the context of the present description, a single semiconductorplatform may refer to a sole unitary semiconductor-based integratedcircuit fabricated on a die or chip. It should be noted that the termsingle semiconductor platform may also refer to multi-chip modules withincreased connectivity which simulate on-chip operation and makesubstantial improvements over utilizing a conventional busimplementation. Of course, the various circuits or devices may also besituated separately or in various combinations of semiconductorplatforms per the desires of the user. Alternately, the parallelprocessing module 525 may be implemented as a circuit board substrateand each of the PPUs 400 and/or memories 404 may be packaged devices. Inan embodiment, the CPU 530, switch 510, and the parallel processingmodule 525 are situated on a single semiconductor platform.

In an embodiment, the signaling rate of each NVLink 410 is 20 to 25Gigabits/second and each PPU 400 includes six NVLink 410 interfaces (asshown in FIG. 5A, five NVLink 410 interfaces are included for each PPU400). Each NVLink 410 provides a data transfer rate of 25Gigabytes/second in each direction, with six links providing 400Gigabytes/second. The NVLinks 410 can be used exclusively for PPU-to-PPUcommunication as shown in FIG. 5A, or some combination of PPU-to-PPU andPPU-to-CPU, when the CPU 530 also includes one or more NVLink 410interfaces.

In an embodiment, the NVLink 410 allows direct load/store/atomic accessfrom the CPU 530 to each PPU's 400 memory 404. In an embodiment, theNVLink 410 supports coherency operations, allowing data read from thememories 404 to be stored in the cache hierarchy of the CPU 530,reducing cache access latency for the CPU 530. In an embodiment, theNVLink 410 includes support for Address Translation Services (ATS),allowing the PPU 400 to directly access page tables within the CPU 530.One or more of the NVLinks 410 may also be configured to operate in alow-power mode.

FIG. 5B illustrates an exemplary system 565 in which the variousarchitecture and/or functionality of the various previous embodimentsmay be implemented. The exemplary system 565 may be configured toimplement the appearance driven automatic 3D modeling system 150 and/orthe method 130 shown in FIG. 1B.

As shown, a system 565 is provided including at least one centralprocessing unit 530 that is connected to a communication bus 575. Thecommunication bus 575 may directly or indirectly couple one or more ofthe following devices: main memory 540, network interface 535, CPU(s)530, display device(s) 545, input device(s) 560, switch 510, andparallel processing system 525. The communication bus 575 may beimplemented using any suitable protocol and may represent one or morelinks or busses, such as an address bus, a data bus, a control bus, or acombination thereof. The communication bus 575 may include one or morebus or link types, such as an industry standard architecture (ISA) bus,an extended industry standard architecture (EISA) bus, a videoelectronics standards association (VESA) bus, a peripheral componentinterconnect (PCI) bus, a peripheral component interconnect express(PCIe) bus, HyperTransport, and/or another type of bus or link. In someembodiments, there are direct connections between components. As anexample, the CPU(s) 530 may be directly connected to the main memory540. Further, the CPU(s) 530 may be directly connected to the parallelprocessing system 525. Where there is direct, or point-to-pointconnection between components, the communication bus 575 may include aPCIe link to carry out the connection. In these examples, a PCI bus neednot be included in the system 565.

Although the various blocks of FIG. 5C are shown as connected via thecommunication bus 575 with lines, this is not intended to be limitingand is for clarity only. For example, in some embodiments, apresentation component, such as display device(s) 545, may be consideredan I/O component, such as input device(s) 560 (e.g., if the display is atouch screen). As another example, the CPU(s) 530 and/or parallelprocessing system 525 may include memory (e.g., the main memory 540 maybe representative of a storage device in addition to the parallelprocessing system 525, the CPUs 530, and/or other components). In otherwords, the computing device of FIG. 5C is merely illustrative.Distinction is not made between such categories as “workstation,”“server,” “laptop,” “desktop,” “tablet,” “client device,” “mobiledevice,” “hand-held device,” “game console,” “electronic control unit(ECU),” “virtual reality system,” and/or other device or system types,as all are contemplated within the scope of the computing device of FIG.5C.

The system 565 also includes a main memory 540. Control logic (software)and data are stored in the main memory 540 which may take the form of avariety of computer-readable media. The computer-readable media may beany available media that may be accessed by the system 565. Thecomputer-readable media may include both volatile and nonvolatile media,and removable and non-removable media. By way of example, and notlimitation, the computer-readable media may comprise computer-storagemedia and communication media.

The computer-storage media may include both volatile and nonvolatilemedia and/or removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, and/or other data types.For example, the main memory 540 may store computer-readableinstructions (e.g., that represent a program(s) and/or a programelement(s), such as an operating system. Computer-storage media mayinclude, but is not limited to, RAM, ROM, EEPROM, flash memory or othermemory technology, CD-ROM, digital versatile disks (DVD) or otheroptical disk storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which maybe used to store the desired information and which may be accessed bysystem 565. As used herein, computer storage media does not comprisesignals per se.

The computer storage media may embody computer-readable instructions,data structures, program modules, and/or other data types in a modulateddata signal such as a carrier wave or other transport mechanism andincludes any information delivery media. The term “modulated datasignal” may refer to a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, the computerstorage media may include wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer-readable media.

Computer programs, when executed, enable the system 565 to performvarious functions. The CPU(s) 530 may be configured to execute at leastsome of the computer-readable instructions to control one or morecomponents of the system 565 to perform one or more of the methodsand/or processes described herein. The CPU(s) 530 may each include oneor more cores (e.g., one, two, four, eight, twenty-eight, seventy-two,etc.) that are capable of handling a multitude of software threadssimultaneously. The CPU(s) 530 may include any type of processor, andmay include different types of processors depending on the type ofsystem 565 implemented (e.g., processors with fewer cores for mobiledevices and processors with more cores for servers). For example,depending on the type of system 565, the processor may be an AdvancedRISC Machines (ARM) processor implemented using Reduced Instruction SetComputing (RISC) or an x86 processor implemented using ComplexInstruction Set Computing (CISC). The system 565 may include one or moreCPUs 530 in addition to one or more microprocessors or supplementaryco-processors, such as math co-processors.

In addition to or alternatively from the CPU(s) 530, the parallelprocessing module 525 may be configured to execute at least some of thecomputer-readable instructions to control one or more components of thesystem 565 to perform one or more of the methods and/or processesdescribed herein. The parallel processing module 525 may be used by thesystem 565 to render graphics (e.g., 3D graphics) or perform generalpurpose computations. For example, the parallel processing module 525may be used for General-Purpose computing on GPUs (GPGPU). Inembodiments, the CPU(s) 530 and/or the parallel processing module 525may discretely or jointly perform any combination of the methods,processes and/or portions thereof.

The system 565 also includes input device(s) 560, the parallelprocessing system 525, and display device(s) 545. The display device(s)545 may include a display (e.g., a monitor, a touch screen, a televisionscreen, a heads-up-display (HUD), other display types, or a combinationthereof), speakers, and/or other presentation components. The displaydevice(s) 545 may receive data from other components (e.g., the parallelprocessing system 525, the CPU(s) 530, etc.), and output the data (e.g.,as an image, video, sound, etc.).

The network interface 535 may enable the system 565 to be logicallycoupled to other devices including the input devices 560, the displaydevice(s) 545, and/or other components, some of which may be built in to(e.g., integrated in) the system 565. Illustrative input devices 560include a microphone, mouse, keyboard, joystick, game pad, gamecontroller, satellite dish, scanner, printer, wireless device, etc. Theinput devices 560 may provide a natural user interface (NUI) thatprocesses air gestures, voice, or other physiological inputs generatedby a user. In some instances, inputs may be transmitted to anappropriate network element for further processing. An NUI may implementany combination of speech recognition, stylus recognition, facialrecognition, biometric recognition, gesture recognition both on screenand adjacent to the screen, air gestures, head and eye tracking, andtouch recognition (as described in more detail below) associated with adisplay of the system 565. The system 565 may be include depth cameras,such as stereoscopic camera systems, infrared camera systems, RGB camerasystems, touchscreen technology, and combinations of these, for gesturedetection and recognition. Additionally, the system 565 may includeaccelerometers or gyroscopes (e.g., as part of an inertia measurementunit (IMU)) that enable detection of motion. In some examples, theoutput of the accelerometers or gyroscopes may be used by the system 565to render immersive augmented reality or virtual reality.

Further, the system 565 may be coupled to a network (e.g., atelecommunications network, local area network (LAN), wireless network,wide area network (WAN) such as the Internet, peer-to-peer network,cable network, or the like) through a network interface 535 forcommunication purposes. The system 565 may be included within adistributed network and/or cloud computing environment.

The network interface 535 may include one or more receivers,transmitters, and/or transceivers that enable the system 565 tocommunicate with other computing devices via an electronic communicationnetwork, included wired and/or wireless communications. The networkinterface 535 may include components and functionality to enablecommunication over any of a number of different networks, such aswireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE, ZigBee,etc.), wired networks (e.g., communicating over Ethernet or InfiniBand),low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.), and/or theInternet.

The system 565 may also include a secondary storage (not shown). Thesecondary storage includes, for example, a hard disk drive and/or aremovable storage drive, representing a floppy disk drive, a magnetictape drive, a compact disk drive, digital versatile disk (DVD) drive,recording device, universal serial bus (USB) flash memory. The removablestorage drive reads from and/or writes to a removable storage unit in awell-known manner. The system 565 may also include a hard-wired powersupply, a battery power supply, or a combination thereof (not shown).The power supply may provide power to the system 565 to enable thecomponents of the system 565 to operate.

Each of the foregoing modules and/or devices may even be situated on asingle semiconductor platform to form the system 565. Alternately, thevarious modules may also be situated separately or in variouscombinations of semiconductor platforms per the desires of the user.While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. Thus, the breadth and scope of a preferred embodiment shouldnot be limited by any of the above-described exemplary embodiments, butshould be defined only in accordance with the following claims and theirequivalents.

Example Network Environments

Network environments suitable for use in implementing embodiments of thedisclosure may include one or more client devices, servers, networkattached storage (NAS), other backend devices, and/or other devicetypes. The client devices, servers, and/or other device types (e.g.,each device) may be implemented on one or more instances of theprocessing system 500 of FIG. 5A and/or exemplary system 565 of FIG.5B—e.g., each device may include similar components, features, and/orfunctionality of the processing system 500 and/or exemplary system 565.

Components of a network environment may communicate with each other viaa network(s), which may be wired, wireless, or both. The network mayinclude multiple networks, or a network of networks. By way of example,the network may include one or more Wide Area Networks (WANs), one ormore Local Area Networks (LANs), one or more public networks such as theInternet and/or a public switched telephone network (PSTN), and/or oneor more private networks. Where the network includes a wirelesstelecommunications network, components such as a base station, acommunications tower, or even access points (as well as othercomponents) may provide wireless connectivity.

Compatible network environments may include one or more peer-to-peernetwork environments—in which case a server may not be included in anetwork environment—and one or more client-server networkenvironments—in which case one or more servers may be included in anetwork environment. In peer-to-peer network environments, functionalitydescribed herein with respect to a server(s) may be implemented on anynumber of client devices.

In at least one embodiment, a network environment may include one ormore cloud-based network environments, a distributed computingenvironment, a combination thereof, etc. A cloud-based networkenvironment may include a framework layer, a job scheduler, a resourcemanager, and a distributed file system implemented on one or more ofservers, which may include one or more core network servers and/or edgeservers. A framework layer may include a framework to support softwareof a software layer and/or one or more application(s) of an applicationlayer. The software or application(s) may respectively include web-basedservice software or applications. In embodiments, one or more of theclient devices may use the web-based service software or applications(e.g., by accessing the service software and/or applications via one ormore application programming interfaces (APIs)). The framework layer maybe, but is not limited to, a type of free and open-source software webapplication framework such as that may use a distributed file system forlarge-scale data processing (e.g., “big data”).

A cloud-based network environment may provide cloud computing and/orcloud storage that carries out any combination of computing and/or datastorage functions described herein (or one or more portions thereof).Any of these various functions may be distributed over multiplelocations from central or core servers (e.g., of one or more datacenters that may be distributed across a state, a region, a country, theglobe, etc.). If a connection to a user (e.g., a client device) isrelatively close to an edge server(s), a core server(s) may designate atleast a portion of the functionality to the edge server(s). Acloud-based network environment may be private (e.g., limited to asingle organization), may be public (e.g., available to manyorganizations), and/or a combination thereof (e.g., a hybrid cloudenvironment).

The client device(s) may include at least some of the components,features, and functionality of the example processing system 500 of FIG.5B and/or exemplary system 565 of FIG. 5C. By way of example and notlimitation, a client device may be embodied as a Personal Computer (PC),a laptop computer, a mobile device, a smartphone, a tablet computer, asmart watch, a wearable computer, a Personal Digital Assistant (PDA), anMP3 player, a virtual reality headset, a Global Positioning System (GPS)or device, a video player, a video camera, a surveillance device orsystem, a vehicle, a boat, a flying vessel, a virtual machine, a drone,a robot, a handheld communications device, a hospital device, a gamingdevice or system, an entertainment system, a vehicle computer system, anembedded system controller, a remote control, an appliance, a consumerelectronic device, a workstation, an edge device, any combination ofthese delineated devices, or any other suitable device.

Machine Learning

Deep neural networks (DNNs) developed on processors, such as the PPU 400have been used for diverse use cases, from self-driving cars to fasterdrug development, from automatic image captioning in online imagedatabases to smart real-time language translation in video chatapplications. Deep learning is a technique that models the neurallearning process of the human brain, continually learning, continuallygetting smarter, and delivering more accurate results more quickly overtime. A child is initially taught by an adult to correctly identify andclassify various shapes, eventually being able to identify shapeswithout any coaching. Similarly, a deep learning or neural learningsystem needs to be trained in object recognition and classification forit get smarter and more efficient at identifying basic objects, occludedobjects, etc., while also assigning context to objects.

At the simplest level, neurons in the human brain look at various inputsthat are received, importance levels are assigned to each of theseinputs, and output is passed on to other neurons to act upon. Anartificial neuron or perceptron is the most basic model of a neuralnetwork. In one example, a perceptron may receive one or more inputsthat represent various features of an object that the perceptron isbeing trained to recognize and classify, and each of these features isassigned a certain weight based on the importance of that feature indefining the shape of an object.

A deep neural network (DNN) model includes multiple layers of manyconnected nodes (e.g., perceptrons, Boltzmann machines, radial basisfunctions, convolutional layers, etc.) that can be trained with enormousamounts of input data to quickly solve complex problems with highaccuracy. In one example, a first layer of the DNN model breaks down aninput image of an automobile into various sections and looks for basicpatterns such as lines and angles. The second layer assembles the linesto look for higher level patterns such as wheels, windshields, andmirrors. The next layer identifies the type of vehicle, and the finalfew layers generate a label for the input image, identifying the modelof a specific automobile brand.

Once the DNN is trained, the DNN can be deployed and used to identifyand classify objects or patterns in a process known as inference.Examples of inference (the process through which a DNN extracts usefulinformation from a given input) include identifying handwritten numberson checks deposited into ATM machines, identifying images of friends inphotos, delivering movie recommendations to over fifty million users,identifying and classifying different types of automobiles, pedestrians,and road hazards in driverless cars, or translating human speech inreal-time.

During training, data flows through the DNN in a forward propagationphase until a prediction is produced that indicates a labelcorresponding to the input. If the neural network does not correctlylabel the input, then errors between the correct label and the predictedlabel are analyzed, and the weights are adjusted for each feature duringa backward propagation phase until the DNN correctly labels the inputand other inputs in a training dataset. Training complex neural networksrequires massive amounts of parallel computing performance, includingfloating-point multiplications and additions that are supported by thePPU 400. Inferencing is less compute-intensive than training, being alatency-sensitive process where a trained neural network is applied tonew inputs it has not seen before to classify images, detect emotions,identify recommendations, recognize and translate speech, and generallyinfer new information.

Neural networks rely heavily on matrix math operations, and complexmulti-layered networks require tremendous amounts of floating-pointperformance and bandwidth for both efficiency and speed. With thousandsof processing cores, optimized for matrix math operations, anddelivering tens to hundreds of TFLOPS of performance, the PPU 400 is acomputing platform capable of delivering performance required for deepneural network-based artificial intelligence and machine learningapplications.

Furthermore, images generated applying one or more of the techniquesdisclosed herein may be used to train, test, or certify DNNs used torecognize objects and environments in the real world. Such images mayinclude scenes of roadways, factories, buildings, urban settings, ruralsettings, humans, animals, and any other physical object or real-worldsetting. Such images may be used to train, test, or certify DNNs thatare employed in machines or robots to manipulate, handle, or modifyphysical objects in the real world. Furthermore, such images may be usedto train, test, or certify DNNs that are employed in autonomous vehiclesto navigate and move the vehicles through the real world. Additionally,images generated applying one or more of the techniques disclosed hereinmay be used to convey information to users of such machines, robots, andvehicles.

FIG. 5C illustrates components of an exemplary system 555 that can beused to train and utilize machine learning, in accordance with at leastone embodiment. As will be discussed, various components can be providedby various combinations of computing devices and resources, or a singlecomputing system, which may be under control of a single entity ormultiple entities. Further, aspects may be triggered, initiated, orrequested by different entities. In at least one embodiment training ofa neural network might be instructed by a provider associated withprovider environment 506, while in at least one embodiment trainingmight be requested by a customer or other user having access to aprovider environment through a client device 502 or other such resource.In at least one embodiment, training data (or data to be analyzed by atrained neural network) can be provided by a provider, a user, or athird party content provider 524. In at least one embodiment, clientdevice 502 may be a vehicle or object that is to be navigated on behalfof a user, for example, which can submit requests and/or receiveinstructions that assist in navigation of a device.

In at least one embodiment, requests are able to be submitted across atleast one network 504 to be received by a provider environment 506. Inat least one embodiment, a client device may be any appropriateelectronic and/or computing devices enabling a user to generate and sendsuch requests, such as, but not limited to, desktop computers, notebookcomputers, computer servers, smartphones, tablet computers, gamingconsoles (portable or otherwise), computer processors, computing logic,and set-top boxes. Network(s) 504 can include any appropriate networkfor transmitting a request or other such data, as may include Internet,an intranet, an Ethernet, a cellular network, a local area network(LAN), a wide area network (WAN), a personal area network (PAN), an adhoc network of direct wireless connections among peers, and so on.

In at least one embodiment, requests can be received at an interfacelayer 508, which can forward data to a training and inference manager532, in this example. The training and inference manager 532 can be asystem or service including hardware and software for managing requestsand service corresponding data or content, in at least one embodiment,the training and inference manager 532 can receive a request to train aneural network, and can provide data for a request to a training module512. In at least one embodiment, training module 512 can select anappropriate model or neural network to be used, if not specified by therequest, and can train a model using relevant training data. In at leastone embodiment, training data can be a batch of data stored in atraining data repository 514, received from client device 502, orobtained from a third party provider 524. In at least one embodiment,training module 512 can be responsible for training data. A neuralnetwork can be any appropriate network, such as a recurrent neuralnetwork (RNN) or convolutional neural network (CNN). Once a neuralnetwork is trained and successfully evaluated, a trained neural networkcan be stored in a model repository 516, for example, that may storedifferent models or networks for users, applications, or services, etc.In at least one embodiment, there may be multiple models for a singleapplication or entity, as may be utilized based on a number of differentfactors.

In at least one embodiment, at a subsequent point in time, a request maybe received from client device 502 (or another such device) for content(e.g., path determinations) or data that is at least partiallydetermined or impacted by a trained neural network. This request caninclude, for example, input data to be processed using a neural networkto obtain one or more inferences or other output values,classifications, or predictions, or for at least one embodiment, inputdata can be received by interface layer 508 and directed to inferencemodule 518, although a different system or service can be used as well.In at least one embodiment, inference module 518 can obtain anappropriate trained network, such as a trained deep neural network (DNN)as discussed herein, from model repository 516 if not already storedlocally to inference module 518. Inference module 518 can provide dataas input to a trained network, which can then generate one or moreinferences as output. This may include, for example, a classification ofan instance of input data. In at least one embodiment, inferences canthen be transmitted to client device 502 for display or othercommunication to a user. In at least one embodiment, context data for auser may also be stored to a user context data repository 522, which mayinclude data about a user which may be useful as input to a network ingenerating inferences, or determining data to return to a user afterobtaining instances. In at least one embodiment, relevant data, whichmay include at least some of input or inference data, may also be storedto a local database 534 for processing future requests. In at least oneembodiment, a user can use account information or other information toaccess resources or functionality of a provider environment. In at leastone embodiment, if permitted and available, user data may also becollected and used to further train models, in order to provide moreaccurate inferences for future requests. In at least one embodiment,requests may be received through a user interface to a machine learningapplication 526 executing on client device 502, and results displayedthrough a same interface. A client device can include resources such asa processor 528 and memory 562 for generating a request and processingresults or a response, as well as at least one data storage element 552for storing data for machine learning application 526.

In at least one embodiment a processor 528 (or a processor of trainingmodule 512 or inference module 518) will be a central processing unit(CPU). As mentioned, however, resources in such environments can utilizeGPUs to process data for at least certain types of requests. Withthousands of cores, GPUs, such as PPU 300 are designed to handlesubstantial parallel workloads and, therefore, have become popular indeep learning for training neural networks and generating predictions.While use of GPUs for offline builds has enabled faster training oflarger and more complex models, generating predictions offline impliesthat either request-time input features cannot be used or predictionsmust be generated for all permutations of features and stored in alookup table to serve real-time requests. If a deep learning frameworksupports a CPU-mode and a model is small and simple enough to perform afeed-forward on a CPU with a reasonable latency, then a service on a CPUinstance could host a model. In this case, training can be done offlineon a GPU and inference done in real-time on a CPU. If a CPU approach isnot viable, then a service can run on a GPU instance. Because GPUs havedifferent performance and cost characteristics than CPUs, however,running a service that offloads a runtime algorithm to a GPU can requireit to be designed differently from a CPU based service.

In at least one embodiment, video data can be provided from clientdevice 502 for enhancement in provider environment 506. In at least oneembodiment, video data can be processed for enhancement on client device502. In at least one embodiment, video data may be streamed from a thirdparty content provider 524 and enhanced by third party content provider524, provider environment 506, or client device 502. In at least oneembodiment, video data can be provided from client device 502 for use astraining data in provider environment 506.

In at least one embodiment, supervised and/or unsupervised training canbe performed by the client device 502 and/or the provider environment506. In at least one embodiment, a set of training data 514 (e.g.,classified or labeled data) is provided as input to function as trainingdata. In at least one embodiment, training data can include instances ofat least one type of object for which a neural network is to be trained,as well as information that identifies that type of object. In at leastone embodiment, training data might include a set of images that eachincludes a representation of a type of object, where each image alsoincludes, or is associated with, a label, metadata, classification, orother piece of information identifying a type of object represented in arespective image. Various other types of data may be used as trainingdata as well, as may include text data, audio data, video data, and soon. In at least one embodiment, training data 514 is provided astraining input to a training module 512. In at least one embodiment,training module 512 can be a system or service that includes hardwareand software, such as one or more computing devices executing a trainingapplication, for training a neural network (or other model or algorithm,etc.). In at least one embodiment, training module 512 receives aninstruction or request indicating a type of model to be used fortraining, in at least one embodiment, a model can be any appropriatestatistical model, network, or algorithm useful for such purposes, asmay include an artificial neural network, deep learning algorithm,learning classifier, Bayesian network, and so on. In at least oneembodiment, training module 512 can select an initial model, or otheruntrained model, from an appropriate repository 516 and utilize trainingdata 514 to train a model, thereby generating a trained model (e.g.,trained deep neural network) that can be used to classify similar typesof data, or generate other such inferences. In at least one embodimentwhere training data is not used, an appropriate initial model can stillbe selected for training on input data per training module 512.

In at least one embodiment, a model can be trained in a number ofdifferent ways, as may depend in part upon a type of model selected. Inat least one embodiment, a machine learning algorithm can be providedwith a set of training data, where a model is a model artifact createdby a training process. In at least one embodiment, each instance oftraining data contains a correct answer (e.g., classification), whichcan be referred to as a target or target attribute. In at least oneembodiment, a learning algorithm finds patterns in training data thatmap input data attributes to a target, an answer to be predicted, and amachine learning model is output that captures these patterns. In atleast one embodiment, a machine learning model can then be used toobtain predictions on new data for which a target is not specified.

In at least one embodiment, training and inference manager 532 canselect from a set of machine learning models including binaryclassification, multiclass classification, generative, and regressionmodels. In at least one embodiment, a type of model to be used candepend at least in part upon a type of target to be predicted.

Graphics Processing Pipeline

In an embodiment, the PPU 400 comprises a graphics processing unit(GPU). The PPU 400 is configured to receive commands that specify shaderprograms for processing graphics data. Graphics data may be defined as aset of primitives such as points, lines, triangles, quads, trianglestrips, and the like. Typically, a primitive includes data thatspecifies a number of vertices for the primitive (e.g., in a model-spacecoordinate system) as well as attributes associated with each vertex ofthe primitive. The PPU 400 can be configured to process the graphicsprimitives to generate a frame buffer (e.g., pixel data for each of thepixels of the display).

An application writes model data for a scene (e.g., a collection ofvertices and attributes) to a memory such as a system memory or memory404. The model data defines each of the objects that may be visible on adisplay. The application then makes an API call to the driver kernelthat requests the model data to be rendered and displayed. The driverkernel reads the model data and writes commands to the one or morestreams to perform operations to process the model data. The commandsmay reference different shader programs to be implemented on theprocessing units within the PPU 400 including one or more of a vertexshader, hull shader, domain shader, geometry shader, and a pixel shader.For example, one or more of the processing units may be configured toexecute a vertex shader program that processes a number of verticesdefined by the model data. In an embodiment, the different processingunits may be configured to execute different shader programsconcurrently. For example, a first subset of processing units may beconfigured to execute a vertex shader program while a second subset ofprocessing units may be configured to execute a pixel shader program.The first subset of processing units processes vertex data to produceprocessed vertex data and writes the processed vertex data to the L2cache 460 and/or the memory 404. After the processed vertex data israsterized (e.g., transformed from three-dimensional data intotwo-dimensional data in screen space) to produce fragment data, thesecond subset of processing units executes a pixel shader to produceprocessed fragment data, which is then blended with other processedfragment data and written to the frame buffer in memory 404. The vertexshader program and pixel shader program may execute concurrently,processing different data from the same scene in a pipelined fashionuntil all of the model data for the scene has been rendered to the framebuffer. Then, the contents of the frame buffer are transmitted to adisplay controller for display on a display device.

FIG. 6A is a conceptual diagram of a graphics processing pipeline 600implemented by the PPU 400 of FIG. 4 , in accordance with an embodiment.The graphics processing pipeline 600 is an abstract flow diagram of theprocessing steps implemented to generate 2D computer-generated imagesfrom 3D geometry data. As is well-known, pipeline architectures mayperform long latency operations more efficiently by splitting up theoperation into a plurality of stages, where the output of each stage iscoupled to the input of the next successive stage. Thus, the graphicsprocessing pipeline 600 receives input data 601 that is transmitted fromone stage to the next stage of the graphics processing pipeline 600 togenerate output data 602. In an embodiment, the graphics processingpipeline 600 may represent a graphics processing pipeline defined by theOpenGL® API. As an option, the graphics processing pipeline 600 may beimplemented in the context of the functionality and architecture of theprevious Figures and/or any subsequent Figure(s).

As shown in FIG. 6A, the graphics processing pipeline 600 comprises apipeline architecture that includes a number of stages. The stagesinclude, but are not limited to, a data assembly stage 610, a vertexshading stage 620, a primitive assembly stage 630, a geometry shadingstage 640, a viewport scale, cull, and clip (VSCC) stage 650, arasterization stage 660, a fragment shading stage 670, and a rasteroperations stage 680. In an embodiment, the input data 601 comprisescommands that configure the processing units to implement the stages ofthe graphics processing pipeline 600 and geometric primitives (e.g.,points, lines, triangles, quads, triangle strips or fans, etc.) to beprocessed by the stages. The output data 602 may comprise pixel data(e.g., color data) that is copied into a frame buffer or other type ofsurface data structure in a memory.

The data assembly stage 610 receives the input data 601 that specifiesvertex data for high-order surfaces, primitives, or the like. The dataassembly stage 610 collects the vertex data in a temporary storage orqueue, such as by receiving a command from the host processor thatincludes a pointer to a buffer in memory and reading the vertex datafrom the buffer. The vertex data is then transmitted to the vertexshading stage 620 for processing.

The vertex shading stage 620 processes vertex data by performing a setof operations (e.g., a vertex shader or a program) once for each of thevertices. Vertices may be, e.g., specified as a 4-coordinate vector(e.g., <x, y, z, w>) associated with one or more vertex attributes(e.g., color, texture coordinates, surface normal, etc.). The vertexshading stage 620 may manipulate individual vertex attributes such asposition, color, texture coordinates, and the like. In other words, thevertex shading stage 620 performs operations on the vertex coordinatesor other vertex attributes associated with a vertex. Such operationscommonly including lighting operations (e.g., modifying color attributesfor a vertex) and transformation operations (e.g., modifying thecoordinate space for a vertex). For example, vertices may be specifiedusing coordinates in an object-coordinate space, which are transformedby multiplying the coordinates by a matrix that translates thecoordinates from the object-coordinate space into a world space or anormalized-device-coordinate (NCD) space. The vertex shading stage 620generates transformed vertex data that is transmitted to the primitiveassembly stage 630.

The primitive assembly stage 630 collects vertices output by the vertexshading stage 620 and groups the vertices into geometric primitives forprocessing by the geometry shading stage 640. For example, the primitiveassembly stage 630 may be configured to group every three consecutivevertices as a geometric primitive (e.g., a triangle) for transmission tothe geometry shading stage 640. In some embodiments, specific verticesmay be reused for consecutive geometric primitives (e.g., twoconsecutive triangles in a triangle strip may share two vertices). Theprimitive assembly stage 630 transmits geometric primitives (e.g., acollection of associated vertices) to the geometry shading stage 640.

The geometry shading stage 640 processes geometric primitives byperforming a set of operations (e.g., a geometry shader or program) onthe geometric primitives. Tessellation operations may generate one ormore geometric primitives from each geometric primitive. In other words,the geometry shading stage 640 may subdivide each geometric primitiveinto a finer mesh of two or more geometric primitives for processing bythe rest of the graphics processing pipeline 600. The geometry shadingstage 640 transmits geometric primitives to the viewport SCC stage 650.

In an embodiment, the graphics processing pipeline 600 may operatewithin a streaming multiprocessor and the vertex shading stage 620, theprimitive assembly stage 630, the geometry shading stage 640, thefragment shading stage 670, and/or hardware/software associatedtherewith, may sequentially perform processing operations. Once thesequential processing operations are complete, in an embodiment, theviewport SCC stage 650 may utilize the data. In an embodiment, primitivedata processed by one or more of the stages in the graphics processingpipeline 600 may be written to a cache (e.g. L1 cache, a vertex cache,etc.). In this case, in an embodiment, the viewport SCC stage 650 mayaccess the data in the cache. In an embodiment, the viewport SCC stage650 and the rasterization stage 660 are implemented as fixed functioncircuitry.

The viewport SCC stage 650 performs viewport scaling, culling, andclipping of the geometric primitives. Each surface being rendered to isassociated with an abstract camera position. The camera positionrepresents a location of a viewer looking at the scene and defines aviewing frustum that encloses the objects of the scene. The viewingfrustum may include a viewing plane, a rear plane, and four clippingplanes. Any geometric primitive entirely outside of the viewing frustummay be culled (e.g., discarded) because the geometric primitive will notcontribute to the final rendered scene. Any geometric primitive that ispartially inside the viewing frustum and partially outside the viewingfrustum may be clipped (e.g., transformed into a new geometric primitivethat is enclosed within the viewing frustum. Furthermore, geometricprimitives may each be scaled based on a depth of the viewing frustum.All potentially visible geometric primitives are then transmitted to therasterization stage 660.

The rasterization stage 660 converts the 3D geometric primitives into 2Dfragments (e.g. capable of being utilized for display, etc.). Therasterization stage 660 may be configured to utilize the vertices of thegeometric primitives to setup a set of plane equations from whichvarious attributes can be interpolated. The rasterization stage 660 mayalso compute a coverage mask for a plurality of pixels that indicateswhether one or more sample locations for the pixel intercept thegeometric primitive. In an embodiment, z-testing may also be performedto determine if the geometric primitive is occluded by other geometricprimitives that have already been rasterized. The rasterization stage660 generates fragment data (e.g., interpolated vertex attributesassociated with a particular sample location for each covered pixel)that are transmitted to the fragment shading stage 670.

The fragment shading stage 670 processes fragment data by performing aset of operations (e.g., a fragment shader or a program) on each of thefragments. The fragment shading stage 670 may generate pixel data (e.g.,color values) for the fragment such as by performing lighting operationsor sampling texture maps using interpolated texture coordinates for thefragment. The fragment shading stage 670 generates pixel data that istransmitted to the raster operations stage 680.

The raster operations stage 680 may perform various operations on thepixel data such as performing alpha tests, stencil tests, and blendingthe pixel data with other pixel data corresponding to other fragmentsassociated with the pixel. When the raster operations stage 680 hasfinished processing the pixel data (e.g., the output data 602), thepixel data may be written to a render target such as a frame buffer, acolor buffer, or the like.

It will be appreciated that one or more additional stages may beincluded in the graphics processing pipeline 600 in addition to or inlieu of one or more of the stages described above. Variousimplementations of the abstract graphics processing pipeline mayimplement different stages. Furthermore, one or more of the stagesdescribed above may be excluded from the graphics processing pipeline insome embodiments (such as the geometry shading stage 640). Other typesof graphics processing pipelines are contemplated as being within thescope of the present disclosure. Furthermore, any of the stages of thegraphics processing pipeline 600 may be implemented by one or morededicated hardware units within a graphics processor such as PPU 400.Other stages of the graphics processing pipeline 600 may be implementedby programmable hardware units such as the processing unit within thePPU 400.

The graphics processing pipeline 600 may be implemented via anapplication executed by a host processor, such as a CPU. In anembodiment, a device driver may implement an application programminginterface (API) that defines various functions that can be utilized byan application in order to generate graphical data for display. Thedevice driver is a software program that includes a plurality ofinstructions that control the operation of the PPU 400. The API providesan abstraction for a programmer that lets a programmer utilizespecialized graphics hardware, such as the PPU 400, to generate thegraphical data without requiring the programmer to utilize the specificinstruction set for the PPU 400. The application may include an API callthat is routed to the device driver for the PPU 400. The device driverinterprets the API call and performs various operations to respond tothe API call. In some instances, the device driver may performoperations by executing instructions on the CPU. In other instances, thedevice driver may perform operations, at least in part, by launchingoperations on the PPU 400 utilizing an input/output interface betweenthe CPU and the PPU 400. In an embodiment, the device driver isconfigured to implement the graphics processing pipeline 600 utilizingthe hardware of the PPU 400.

Various programs may be executed within the PPU 400 in order toimplement the various stages of the graphics processing pipeline 600.For example, the device driver may launch a kernel on the PPU 400 toperform the vertex shading stage 620 on one processing unit (or multipleprocessing units). The device driver (or the initial kernel executed bythe PPU 400) may also launch other kernels on the PPU 400 to performother stages of the graphics processing pipeline 600, such as thegeometry shading stage 640 and the fragment shading stage 670. Inaddition, some of the stages of the graphics processing pipeline 600 maybe implemented on fixed unit hardware such as a rasterizer or a dataassembler implemented within the PPU 400. It will be appreciated thatresults from one kernel may be processed by one or more interveningfixed function hardware units before being processed by a subsequentkernel on a processing unit.

Images generated applying one or more of the techniques disclosed hereinmay be displayed on a monitor or other display device. In someembodiments, the display device may be coupled directly to the system orprocessor generating or rendering the images. In other embodiments, thedisplay device may be coupled indirectly to the system or processor suchas via a network. Examples of such networks include the Internet, mobiletelecommunications networks, a WIFI network, as well as any other wiredand/or wireless networking system. When the display device is indirectlycoupled, the images generated by the system or processor may be streamedover the network to the display device. Such streaming allows, forexample, video games or other applications, which render images, to beexecuted on a server, a data center, or in a cloud-based computingenvironment and the rendered images to be transmitted and displayed onone or more user devices (such as a computer, video game console,smartphone, other mobile device, etc.) that are physically separate fromthe server or data center. Hence, the techniques disclosed herein can beapplied to enhance the images that are streamed and to enhance servicesthat stream images such as NVIDIA GeForce Now (GFN), Google Stadia, andthe like.

Example Game Streaming System

FIG. 6B is an example system diagram for a game streaming system 605, inaccordance with some embodiments of the present disclosure. FIG. 6Bincludes game server(s) 603 (which may include similar components,features, and/or functionality to the example processing system 500 ofFIG. 5A and/or exemplary system 565 of FIG. 5B), client device(s) 604(which may include similar components, features, and/or functionality tothe example processing system 500 of FIG. 5A and/or exemplary system 565of FIG. 5B), and network(s) 606 (which may be similar to the network(s)described herein). In some embodiments of the present disclosure, thesystem 605 may be implemented.

In the system 605, for a game session, the client device(s) 604 may onlyreceive input data in response to inputs to the input device(s),transmit the input data to the game server(s) 603, receive encodeddisplay data from the game server(s) 603, and display the display dataon the display 624. As such, the more computationally intense computingand processing is offloaded to the game server(s) 603 (e.g.,rendering—in particular ray or path tracing—for graphical output of thegame session is executed by the GPU(s) of the game server(s) 603). Inother words, the game session is streamed to the client device(s) 604from the game server(s) 603, thereby reducing the requirements of theclient device(s) 604 for graphics processing and rendering.

For example, with respect to an instantiation of a game session, aclient device 604 may be displaying a frame of the game session on thedisplay 624 based on receiving the display data from the game server(s)603. The client device 604 may receive an input to one of the inputdevice(s) and generate input data in response. The client device 604 maytransmit the input data to the game server(s) 603 via the communicationinterface 621 and over the network(s) 606 (e.g., the Internet), and thegame server(s) 603 may receive the input data via the communicationinterface 618. The CPU(s) may receive the input data, process the inputdata, and transmit data to the GPU(s) that causes the GPU(s) to generatea rendering of the game session. For example, the input data may berepresentative of a movement of a character of the user in a game,firing a weapon, reloading, passing a ball, turning a vehicle, etc. Therendering component 612 may render the game session (e.g.,representative of the result of the input data) and the render capturecomponent 614 may capture the rendering of the game session as displaydata (e.g., as image data capturing the rendered frame of the gamesession). The rendering of the game session may include ray orpath-traced lighting and/or shadow effects, computed using one or moreparallel processing units—such as GPUs, which may further employ the useof one or more dedicated hardware accelerators or processing cores toperform ray or path-tracing techniques—of the game server(s) 603. Theencoder 616 may then encode the display data to generate encoded displaydata and the encoded display data may be transmitted to the clientdevice 604 over the network(s) 606 via the communication interface 618.The client device 604 may receive the encoded display data via thecommunication interface 621 and the decoder 622 may decode the encodeddisplay data to generate the display data. The client device 604 maythen display the display data via the display 624.

It is noted that the techniques described herein may be embodied inexecutable instructions stored in a computer readable medium for use byor in connection with a processor-based instruction execution machine,system, apparatus, or device. It will be appreciated by those skilled inthe art that, for some embodiments, various types of computer-readablemedia can be included for storing data. As used herein, a“computer-readable medium” includes one or more of any suitable mediafor storing the executable instructions of a computer program such thatthe instruction execution machine, system, apparatus, or device may read(or fetch) the instructions from the computer-readable medium andexecute the instructions for carrying out the described embodiments.Suitable storage formats include one or more of an electronic, magnetic,optical, and electromagnetic format. A non-exhaustive list ofconventional exemplary computer-readable medium includes: a portablecomputer diskette; a random-access memory (RAM); a read-only memory(ROM); an erasable programmable read only memory (EPROM); a flash memorydevice; and optical storage devices, including a portable compact disc(CD), a portable digital video disc (DVD), and the like.

It should be understood that the arrangement of components illustratedin the attached Figures are for illustrative purposes and that otherarrangements are possible. For example, one or more of the elementsdescribed herein may be realized, in whole or in part, as an electronichardware component. Other elements may be implemented in software,hardware, or a combination of software and hardware. Moreover, some orall of these other elements may be combined, some may be omittedaltogether, and additional components may be added while still achievingthe functionality described herein. Thus, the subject matter describedherein may be embodied in many different variations, and all suchvariations are contemplated to be within the scope of the claims.

To facilitate an understanding of the subject matter described herein,many aspects are described in terms of sequences of actions. It will berecognized by those skilled in the art that the various actions may beperformed by specialized circuits or circuitry, by program instructionsbeing executed by one or more processors, or by a combination of both.The description herein of any sequence of actions is not intended toimply that the specific order described for performing that sequencemust be followed. All methods described herein may be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context.

The use of the terms “a” and “an” and “the” and similar references inthe context of describing the subject matter (particularly in thecontext of the following claims) are to be construed to cover both thesingular and the plural, unless otherwise indicated herein or clearlycontradicted by context. The use of the term “at least one” followed bya list of one or more items (for example, “at least one of A and B”) isto be construed to mean one item selected from the listed items (A or B)or any combination of two or more of the listed items (A and B), unlessotherwise indicated herein or clearly contradicted by context.Furthermore, the foregoing description is for the purpose ofillustration only, and not for the purpose of limitation, as the scopeof protection sought is defined by the claims as set forth hereinaftertogether with any equivalents thereof. The use of any and all examples,or exemplary language (e.g., “such as”) provided herein, is intendedmerely to better illustrate the subject matter and does not pose alimitation on the scope of the subject matter unless otherwise claimed.The use of the term “based on” and other like phrases indicating acondition for bringing about a result, both in the claims and in thewritten description, is not intended to foreclose any other conditionsthat bring about that result. No language in the specification should beconstrued as indicating any non-claimed element as essential to thepractice of the invention as claimed.

What is claimed is:
 1. A computer-implemented method for optimizing areference three-dimensional (3D) model, comprising: processing aninitial 3D model to produce a set of images for environmental conditionsspecifying at least one of camera position or light position; renderingthe reference 3D model to produce a set of reference images for theenvironmental conditions; computing image space losses based on the setof images and the set of reference images; and updating parameters ofthe initial 3D model according to the image space losses to produce areduced resolution 3D model having a lower geometric resolution comparedwith a geometric resolution of the reference 3D model, wherein a numberof primitives defining the reduced resolution 3D model is equal orgreater than a number of primitives defining the initial 3D model. 2.The computer-implemented method of claim 1, wherein the parameterscomprise vertex locations and surface normal vectors.
 3. Thecomputer-implemented method of claim 1, wherein a rendering pipelinethat processes the initial 3D model to produce the set of images is adifferentiable renderer and the image space losses are propagatedbackwards through the rendering pipeline to update the parameters. 4.The computer-implemented method of claim 1, wherein the number ofprimitives defining the reduced resolution 3D model is greater than thenumber of primitives defining the initial 3D model.
 5. Thecomputer-implemented method of claim 1, wherein the parameters comprisematerials.
 6. A computer-implemented method for optimizing a referencethree-dimensional (3D) model, comprising: processing an initial 3D modelto produce a set of images for environmental conditions specifying atleast one of camera position or light position; rendering the reference3D model to produce a set of reference images for the environmentalconditions; computing image space losses based on the set of images andthe set of reference images; and updating parameters of the initial 3Dmodel according to the image space losses to produce a reducedresolution 3D model having a lower geometric resolution compared with ageometric resolution of the reference 3D model, wherein the parameterscomprise a normal map and geometric details of the reduced resolution 3Dmodel are encoded into the normal map.
 7. The computer-implementedmethod of claim 1, wherein the parameters comprise spatially varyingmaterial parameters that approximate geometric details of the reducedresolution 3D model.
 8. The computer-implemented method of claim 1,wherein the reduced 3D model comprises a first geometric representationand the reference 3D model comprises a second geometric representationthat is different from the first geometric representation.
 9. Thecomputer-implemented method of claim 1, wherein the initial 3D modelcomprises a sphere.
 10. The computer-implemented method of claim 1,wherein the lower geometric resolution comprises a lower number ofgeometric primitives.
 11. The computer-implemented method of claim 10,wherein the lower geometric resolution further comprises a lower numberof vertices.
 12. The computer-implemented method of claim 1, wherein thelower geometric resolution comprises a lower number of vertices.
 13. Thecomputer-implemented method of claim 1, wherein the set of referenceimages comprise an animation sequence and the parameters comprise atleast one of skinning weights and bone transformations.
 14. Thecomputer-implemented method of claim 1, wherein at least one of thesteps of processing, rendering, computing, and updating are performedwithin a cloud computing environment.
 15. The computer-implementedmethod of claim 1, wherein at least one of the steps of processing,rendering, computing, and updating are performed on a server or in adata center to generate the set of images and the reduced resolution 3Dmodel is streamed to a user device.
 16. The computer-implemented methodof claim 1, wherein at least one of the steps of processing, rendering,computing, and updating are performed for training, testing, orcertifying a neural network employed in a machine, robot, or autonomousvehicle.
 17. The computer-implemented method of claim 1, wherein atleast one of the steps of processing, rendering, computing, and updatingis performed on a virtual machine comprising a portion of a graphicsprocessing unit.
 18. A system, comprising: a processor that optimizes areference three-dimensional (3D) model by: processing an initial 3Dmodel to produce a set of images for environmental conditions specifyingat least one of camera position or light position; rendering thereference 3D model to produce a set of reference images for theenvironmental conditions; computing image space losses based on the setof images and the set of reference images; and updating parameters ofthe initial 3D model according to the image space losses to produce areduced resolution 3D model having a lower geometric resolution comparedwith a geometric resolution of the reference 3D model, wherein a numberof primitives defining the reduced resolution 3D model is equal orgreater than a number of primitives defining the initial 3D model. 19.The system of claim 18, wherein the lower geometric resolution comprisesat least one of a lower number of vertices and a lower number ofprimitives.
 20. The system of claim 18, wherein the lower geometricresolution comprises both a lower number of vertices and a lower numberof primitives.
 21. A non-transitory computer-readable media storingcomputer instructions for optimizing a reference three-dimensional (3D)model that, when executed by one or more processors, cause the one ormore processors to perform the steps of: processing an initial 3D modelto produce a set of images for environmental conditions specifying atleast one of camera position or light position; rendering the reference3D model to produce the set of reference images for the environmentalconditions; computing image space losses based on a set of images andthe set of reference images; and updating parameters of the initial 3Dmodel according to the image space losses to produce a reducedresolution 3D model having a lower geometric resolution compared with ageometric resolution of the reference 3D model, wherein a number ofprimitives defining the reduced resolution 3D model is equal or greaterthan a number of primitives defining the initial 3D model.
 22. A system,comprising: a processor that optimizes a reference three-dimensional(3D) model by: processing an initial 3D model to produce a set of imagesfor environmental conditions specifying at least one of camera positionor light position; rendering the reference 3D model to produce a set ofreference images for the environmental conditions; computing image spacelosses based on the set of images and the set of reference images; andupdating parameters of the initial 3D model according to the image spacelosses to produce a reduced resolution 3D model having a lower geometricresolution compared with a geometric resolution of the reference 3Dmodel, wherein the parameters comprise a normal map and geometricdetails of the reduced resolution 3D model are encoded into the normalmap.
 23. A non-transitory computer-readable media storing computerinstructions for optimizing a reference three-dimensional (3D) modelthat, when executed by one or more processors, cause the one or moreprocessors to perform the steps of: processing an initial 3D model toproduce a set of images for environmental conditions specifying at leastone of camera position or light position; rendering the reference 3Dmodel to produce the set of reference images for the environmentalconditions; computing image space losses based on a set of images andthe set of reference images; and updating parameters of the initial 3Dmodel according to the image space losses to produce a reducedresolution 3D model having a lower geometric resolution compared with ageometric resolution of the reference 3D model, wherein the parameterscomprise a normal map and geometric details of the reduced resolution 3Dmodel are encoded into the normal map.